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. Although some third-party vendor information may be proprietary, the evaluation team should still review these processes and establish safeguards for vendors.

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Marktechpost

AUGUST 7, 2024

The Role of Explainable AI in In Vitro Diagnostics Under European Regulations: AI is increasingly critical in healthcare, especially in vitro diagnostics (IVD). The European IVDR recognizes software, including AI and ML algorithms, as part of IVDs. This includes considering patient population, disease conditions, and scanning quality.

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Towards AI

OCTOBER 30, 2024

Microsoft’s OmniParser is thoroughly documented in an accompanying paper, which explains each component in a clear and accessible way. It simply provides instructions like “click here” or “navigate to that option” based on information from sources like Stack Overflow. Image from the author Now, we want to take this a step further.

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Towards AI

OCTOBER 10, 2024

The evolution of Large Language Models (LLMs) allowed for the next level of understanding and information extraction that classical NLP algorithms struggle with. Reviews include product and user information, ratings, and a plaintext review. Advances in Neural Information Processing Systems 28 (NIPS 2015).

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AI News

OCTOBER 8, 2024

At the University of Maryland (UMD), interdisciplinary teams tackle the complex interplay between normative reasoning, machine learning algorithms, and socio-technical systems. They aim to create AI systems that can learn rules from data while maintaining explainable decision-making processes grounded in legal and normative reasoning.

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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. LayerCAM aggregates information across multiple layers for a more detailed heatmap. So, let’s import the libraries.

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Pickl AI

AUGUST 23, 2024

Summary: This blog post delves into the importance of explainability and interpretability in AI, covering definitions, challenges, techniques, tools, applications, best practices, and future trends. For instance, if a model predicts that a loan application should be denied, explainability seeks to clarify the rationale behind this decision.

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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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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. Ultimately these definitions end up being almost circular!

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ODSC - Open Data Science

NOVEMBER 30, 2023

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? Explainable AI (XAI) refers to AI that explains how, where, and why it produces decisions.

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Unite.AI

JULY 24, 2023

The YOLO concept was first introduced in 2016 by Joseph Redmon, and it was the talk of the town almost instantly because it was much quicker, and much more accurate than the existing object detection algorithms. It wasn’t long before the YOLO algorithm became a standard in the computer vision industry. How Does YOLO Work?

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IBM Journey to AI blog

AUGUST 9, 2024

Predictive AI blends statistical analysis with machine learning algorithms to find data patterns and forecast future outcomes. In short, predictive AI helps enterprises make informed decisions regarding the next step to take for their business. What is predictive AI? What’s the difference between generative AI and predictive AI?

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PyImageSearch

AUGUST 5, 2024

Probabilistic search emerges as a powerful tool in the realm of information retrieval and web search. Unlike traditional semantic or Boolean search methods, which rely on exact matches or binary logic, probabilistic search takes into account the uncertainty and relevance of information.

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Marktechpost

AUGUST 1, 2024

Large language models (LLMs) have seen rapid advancements, making significant strides in algorithmic problem-solving tasks. These models are being integrated into algorithms to serve as general-purpose solvers, enhancing their performance and efficiency.

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Marktechpost

MARCH 22, 2024

There are major worries about data traceability and reproducibility because, unlike code, data modifications do not always provide enough information about the exact source data used to create the published data and the transformations made to each source. This information will then be indexed as part of a data catalog.

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Unite.AI

MARCH 14, 2024

For many institutional investors, the answer is likely to be no – that the potential benefits of AI just aren’t worth the risk associated with a process they aren’t able to understand, much less explain to their boards and clients. But there is a way out of this dilemma.

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AWS Machine Learning Blog

JANUARY 25, 2023

Model explainability refers to the process of relating the prediction of a machine learning (ML) model to the input feature values of an instance in humanly understandable terms. This field is often referred to as explainable artificial intelligence (XAI). In this post, we illustrate the use of Clarify for explaining NLP models.

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Unite.AI

NOVEMBER 14, 2024

“Our initial question was whether we could combine the best of both sensing modalities,” explains Mingmin Zhao, Assistant Professor in Computer and Information Science. “Our signal processing and machine learning algorithms are able to extract rich 3D information from the environment.”

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Mlearning.ai

MARCH 22, 2023

Principal Component Analysis(PCA) is an essential algorithm in a data scientist's toolkit. It is used to reduce the dimensionality of a dataset while retaining as much of the original information as possible. Unfortunately, this concept is complex and can’t be explained as well using the written word alone.

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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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Pickl AI

SEPTEMBER 16, 2024

Summary: This blog highlights ten crucial Machine Learning algorithms to know in 2024, including linear regression, decision trees, and reinforcement learning. Each algorithm is explained with its applications, strengths, and weaknesses, providing valuable insights for practitioners and enthusiasts in the field.

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Marktechpost

MARCH 23, 2024

Although deep features have many applications in computer vision, they often need more spatial resolution to directly perform dense prediction tasks like segmentation and depth prediction due to models that aggressively pool information by models over large areas. It provides sub-feature information to train the upsampler.

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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

NOVEMBER 16, 2024

It also provides information on the different deep learning techniques used in various industrial applications. Machine Learning (in Python and R) for Dummies This book explains the fundamentals of machine learning by providing practical examples using Python and R.

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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. Explainable AI not only resolves this for us.

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Google Research AI blog

AUGUST 24, 2023

Thus, an important question is whether LLMs are capable of algorithmic reasoning, which involves solving a task by applying a set of abstract rules that define the algorithm. Finally, we demonstrate that a model can reliably execute algorithms on out-of-distribution examples with an appropriate choice of prompting strategy.

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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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Unite.AI

MAY 27, 2024

AI, on the other hand, leverages machine learning (ML) algorithms that can analyze vast amounts of data, including transaction history, location, and device information, to identify anomalies and suspicious activity in real-time. One of the key challenges in AI is explainability.

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Pickl AI

JULY 2, 2024

Read More: Data Abstraction and Encapsulation in Python Explained. This versatility allows users to document processes clearly, explain methodologies effectively, and provide crucial context to code snippets. The output will show the information on your Python version. Introduction to Model validation in Python.

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Bugra Akyildiz

MARCH 30, 2024

Memory Bottleneck: Storing information about all previous tokens requires a lot of memory, making it difficult to handle very long sequences. It employs a Control Theory-inspired State Space Model (SSM) that focuses on relevant information from the past instead of attending to everything.

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Mlearning.ai

FEBRUARY 24, 2023

In this blog post, we will explain what PCA is and how it works and present five examples of PCA analysis. Principal Component Analysis (PCA) is extracting the most important information from a data set by reducing the number of variables. The eigenvalues represent the amount of variance explained by the corresponding eigenvector.

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Unite.AI

NOVEMBER 22, 2024

It helps explain how AI models, especially LLMs, process information and make decisions. This makes it easier to track how the AI processes and prioritizes information. Mapping Information Flow Gemma Scope can help track the flow of data through a model by analyzing activation signals at each layer.

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Pickl AI

JUNE 2, 2023

One of the most popular algorithms in Machine Learning are the Decision Trees that are useful in regression and classification tasks. In Supervised Learning, Decision Trees are the Machine Learning algorithms where you can split data continuously based on a specific parameter. How Decision Tree Algorithm works?

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Unite.AI

APRIL 9, 2024

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. Explainability is essential for accountability, fairness, and user confidence. Transparency is fundamental for responsible AI usage.

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Unite.AI

DECEMBER 13, 2023

Importance of Staying Updated on Trends Staying updated on AI trends is crucial because it keeps you informed about the latest advancements, ensuring you remain at the forefront of technological innovation. Regulatory Compliance and Explainability Regulatory bodies are focusing on transparency and accountability.

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Unite.AI

SEPTEMBER 15, 2024

Professor Thompson explains, “We're essentially using the inherent wiggling and jiggling of atoms to process and store information.” In conventional computers, information is processed and stored using binary states – on or off, 1 or 0.

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IBM Journey to AI blog

JANUARY 8, 2024

With OLGA, judges and clerks can sift through thousands of documents faster and use specific search criteria to find relevant information from various documents. Additionally, the system would provide information on the lawsuit to contextualize the information that surfaced from the search. Explainability will play a key role.

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