Towards AI

FEBRUARY 13, 2024

Author(s): Stavros Theocharis Originally published on Towards AI. Introduction It’s been a while since I created this package ‘easy-explain’ and published it on Pypi. GradCam is a widely used Explainable AI method that has been extensively discussed in both forums and literature. So, let’s import the libraries.

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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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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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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

As a result, it becomes necessary for humans to comprehend these algorithms and their workings on a deeper level. This is where Interpretable (IAI) and Explainable (XAI) Artificial Intelligence techniques come into play, and the need to understand their differences become more apparent.

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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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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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IBM Journey to AI blog

APRIL 11, 2023

The new rules, which passed in December 2021 with enforcement , will require organizations that use algorithmic HR tools to conduct a yearly bias audit. This means that processes utilizing algorithmic AI and automation should be carefully scrutinized and tested for impact according to the specific regulations in each state, city, or locality.

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AI News

OCTOBER 8, 2024

As artificial intelligence systems increasingly permeate critical decision-making processes in our everyday lives, the integration of ethical frameworks into AI development is becoming a research priority. So, in this field, they developed algorithms to extract information from the data.

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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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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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IBM Journey to AI blog

AUGUST 9, 2024

What is predictive AI? Predictive AI blends statistical analysis with machine learning algorithms to find data patterns and forecast future outcomes. These adversarial AI algorithms encourage the model to generate increasingly high-quality outputs.

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AI Weekly

OCTOBER 3, 2024

From ChatGP through to AI video generators, the lines between technology and parts of our lives have become increasingly blurred. theconversation.com Dangers of AI: Exploring the risks and threats Living in this fast-forwarding, digital world, artificial intelligence is bringing a revolution to industries and lifestyles.

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Unite.AI

JUNE 26, 2023

Similarly, what if a drug diagnosis algorithm recommends the wrong medication for a patient and they suffer a negative side effect? 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.

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Pickl AI

AUGUST 28, 2024

Summary: This comprehensive guide covers the basics of classification algorithms, key techniques like Logistic Regression and SVM, and advanced topics such as handling imbalanced datasets. Classification algorithms are crucial in various industries, from spam detection in emails to medical diagnosis and customer segmentation.

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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

JUNE 26, 2023

Possibilities are growing that include assisting in writing articles, essays or emails; accessing summarized research; generating and brainstorming ideas; dynamic search with personalized recommendations for retail and travel; and explaining complicated topics for education and training. What is generative AI?

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Unite.AI

OCTOBER 20, 2023

In addition, they can use group and individual fairness techniques to ensure that algorithms treat different groups and individuals fairly. Promote AI transparency and explainability: AI transparency means it is easy to understand how AI models work and make decisions.

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IBM Journey to AI blog

JANUARY 25, 2024

Yet many AI creators are currently facing backlash for the biases, inaccuracies and problematic data practices being exposed in their models. These issues require more than a technical, algorithmic or AI-based solution. Consider, for example, who benefits most from content-recommendation algorithms and search engine algorithms.

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Unite.AI

SEPTEMBER 20, 2024

Key milestones in this evolution include the advent of algorithmic trading in the late 1980s and early 1990s, where simple algorithms automated trades based on set criteria. Over the past ten years, AI has become a reality in financial analysis. A key feature of Palmyra-Fin is its ability to perform real-time market analysis.

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Unite.AI

JULY 1, 2024

Transparency and Explainability Enhancing transparency and explainability is essential. Techniques such as model interpretability frameworks and Explainable AI (XAI) help auditors understand decision-making processes and identify potential issues.

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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. Now Algorithms know what they are doing and why! Let us go further into the enigmas of Artificial Intelligence, where AI is making waves like never before! SOURCE: [link] A.

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Unite.AI

DECEMBER 13, 2023

Ultimately, staying updated empowers enthusiasts to leverage the full potential of AI and make confident decisions in their professional and personal pursuits. AI-Powered Threat Detection and Response AI takes the lead in making the digital world safer.

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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. Documented, explainable model facts are necessary when defending analytic decisions.

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Flipboard

NOVEMBER 4, 2024

Explainable AI techniques were employed to assess the AI’s decision-making processes. The study concludes that the YOLOv5s algorithm effectively detects dental decay on intraoral photographs and performs comparably to junior dentists. in detecting dental decay. In contrast, junior dentists achieved 83.3%

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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. Aircraft owners or technicians set up the algorithm with airplane data, including its key systems and typical performance metrics. Black-box AI poses a serious concern in the aviation industry.

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AI Weekly

OCTOBER 12, 2023

pitneybowes.com In The News AMD to acquire AI software startup in effort to catch Nvidia AMD said on Tuesday it plans to buy an artificial intelligence startup called Nod.ai nature.com Ethics The world's first real AI rules are coming soon. [Get your FREE REPORT.] as part of an effort to bolster its software capabilities.

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IBM Journey to AI blog

OCTOBER 12, 2023

They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources. Explainable AI — Explainable AI is achieved when an organization can confidently and clearly state what data an AI model used to perform its tasks.

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Unite.AI

SEPTEMBER 23, 2024

In some cases, the drugs AI helps discover may not pass regulatory scrutiny, or they may fail in the later stages of clinical trials — something we’ve seen before with traditional drug development methods. One major hurdle is the ‘black box’ nature of AI algorithms. Another challenge is the data itself.

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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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Towards AI

JULY 13, 2024

This paper explores a reinforcement learning approach using the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize pricing strategies. This combined approach enhances the traditional pricing model by incorporating real-time analysis and explainable AI techniques.

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Dlabs.ai

JULY 19, 2023

Example: Algorithmic Bias in the UK A-level Grading To illustrate, consider a real-world example that occurred during the COVID-19 pandemic in the UK. With the traditional A-level exams canceled due to health concerns, the UK government used an algorithm to determine student grades.

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NYU Center for Data Science

JUNE 16, 2023

Home to both a thriving tech ecosystem and pioneering efforts on regulating algorithm decision-making systems, New York City provides a vibrant research environment and a plethora of interdisciplinary collaborators,” said Umang. For these reasons, I am excited to start my academic journey at NYU. By Meryl Phair

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Towards AI

OCTOBER 3, 2024

Ongoing Challenges: – Design Complexity: Designing and training these complex networks remains a hurdle due to their intricate architectures and the need for specialized algorithms.– These chips have demonstrated the ability to process complex algorithms using a fraction of the energy required by traditional GPUs.–

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Unite.AI

NOVEMBER 22, 2024

This “black box” nature of AI raises concerns about fairness, reliability, and trust—especially in fields that rely heavily on transparent and accountable systems. It helps explain how AI models, especially LLMs, process information and make decisions. Finally, Gemma Scope plays a role in improving AI safety.

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Pickl AI

SEPTEMBER 17, 2024

Machine Learning (ML) is a subset of AI that focuses on developing algorithms and statistical models that enable systems to perform specific tasks effectively without being explicitly programmed. Explain The Concept of Supervised and Unsupervised Learning. Explain The Concept of Supervised and Unsupervised Learning.

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