Towards AI

MARCH 17, 2024

It is also garnering massive popularity in organizations and enterprises, with every corner of every business implementing LLMs, Stable Diffusion, and the next trendy AI product. Alongside this, there is a second boom in XAI or Explainable AI. Interpretability — Explaining the meaning of a model/model decisions to humans.

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Towards AI

FEBRUARY 13, 2024

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. The plotting functionality is also included, so you only need to run a few lines of code.

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Analytics Vidhya

MAY 26, 2022

Introduction The ability to explain decisions is increasingly becoming important across businesses. Explainable AI is no longer just an optional add-on when using ML algorithms for corporate decision making. While there are a lot of techniques that have been developed for supervised algorithms, […].

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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. 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.

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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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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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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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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

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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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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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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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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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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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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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. One of the main risks associated with AI is its black-box nature.

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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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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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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 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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AI Weekly

OCTOBER 12, 2023

coindesk.com Chorus of creative workers demands AI regulation at FTC roundtable At a virtual Federal Trade Commission (FTC) roundtable yesterday, a deep lineup of creative workers and labor leaders representing artists demanded AI regulation of generative AI models and tools.

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IBM Journey to AI blog

JUNE 26, 2023

What is generative AI? Generative AI uses an advanced form of machine learning algorithms that takes users prompts and uses natural language processing (NLP) to generate answers to almost any question asked. You can start by learning more about the advances IBM is making in new generative AI models with watsonx.ai

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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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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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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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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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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

MARCH 18, 2024

Some of key ingredients of such an approach are highlighted below: Robust Data Verification : This step entails implementation of stringent processes to validate the accuracy, relevance, and quality of the data, filtering out harmful AI-generated content before it reaches AI models.

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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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IBM Journey to AI blog

JULY 6, 2023

Machine learning works on a known problem with tools and techniques, creating algorithms that let a machine learn from data through experience and with minimal human intervention. Deep learning algorithms are neural networks modeled after the human brain. Some people worry that AI and machine learning will eliminate jobs.

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Unite.AI

SEPTEMBER 13, 2023

Mystery and Skepticism In generative AI, the concept of understanding how an LLM gets from Point A – the input – to Point B – the output – is far more complex than with non-generative algorithms that run along more set patterns. Additionally, the continuously expanding datasets used by ML algorithms complicate explainability further.

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Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. Basics of Machine Learning Machine Learning is a subset of Artificial Intelligence (AI) that allows systems to learn from data, improve from experience, and make predictions or decisions without being explicitly programmed.

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How to Learn Machine Learning

JUNE 9, 2024

This technology streamlines the model-building process while simultaneously increasing productivity by determining the best algorithms for specific data sets. It is quite beneficial for organizations looking to capitalize on the potential of AI without making significant investments.

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Pickl AI

NOVEMBER 5, 2024

Data Science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. For example, PayPal uses Machine Learning algorithms to analyse transaction patterns and identify anomalies that may indicate fraudulent activity.

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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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