Unite.AI

OCTOBER 25, 2024

SLK's AI-powered platforms and accelerators are designed to automate and streamline processes, helping businesses reach the market more quickly. In mortgage requisition intake, AI optimizes efficiency by automating the analysis of requisition data, leading to faster processing times.

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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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IBM Journey to AI blog

FEBRUARY 8, 2023

For many of today’s organizations today, governing AI requires a lot of manual work that include the use of multiple tools, applications and platforms. Lack of automation can lead to lengthy model approval, validation and deployment cycles during which model drift and bias can happen.

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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. AI automates processes, reducing human error and operational costs. The impact is profound and far-reaching.

Data Science 52

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Unite.AI

JULY 1, 2024

Automated tools can streamline this process, allowing real-time audits and timely interventions. Transparency and Explainability Enhancing transparency and explainability is essential. This includes using diverse training datasets, employing fairness-aware algorithms, and regularly assessing models for biases.

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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.

AI 290

AI AI Explainability Responsible AI 290

Pickl AI

SEPTEMBER 17, 2024

Summary : AI is transforming the cybersecurity landscape by enabling advanced threat detection, automating security processes, and adapting to new threats. Introduction In the rapidly evolving landscape of cybersecurity, Artificial Intelligence (AI) has emerged as a powerful tool in the fight against cyber threats.

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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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IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. What is MLOps?

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How to Learn Machine Learning

JUNE 9, 2024

The brief yet convincing answer to these questions is the ability of ML solutions to automate routine tasks and facilitate decision-making. 5 Top Machine Learning Trends in 2024 Here are the top 5 machine learning trends that you must watch for in 2024: 1) AutoML Some people refer to Automated Machine Learning as AutoML.

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IBM Journey to AI blog

JULY 17, 2023

While traditional AI approaches provide customers with quick service, they have their limitations. Currently chat bots are relying on rule-based systems or traditional machine learning algorithms (or models) to automate tasks and provide predefined responses to customer inquiries. Watsonx.ai

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Marktechpost

MAY 12, 2024

It is based on adjustable and explainable AI technology. The technology provides automated, improved machine-learning techniques for fraud identification and proactive enforcement to reduce fraud and block rates. Its initial AI algorithm is designed to detect errors in data, calculations, and financial predictions.

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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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Data Scientist Metadata Responsible AI Explainability 253

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

Machine Learning 102

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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

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.

Data Science 200

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Unite.AI

APRIL 22, 2024

AI-driven telehealth platforms, employing tools like chatbots, autonomously handle patient interactions, schedule appointments, and deliver medical information. With more than 13 million global users, Ada Health exemplifies transparent, explainable AI in healthcare, providing clear insights into the diagnostic process.

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Pickl AI

SEPTEMBER 3, 2024

Explainable AI As ANNs are increasingly used in critical applications, such as healthcare and finance, the need for transparency and interpretability has become paramount. ANNs are being deployed on edge devices to enable real-time decision-making in applications such as smart cities, autonomous vehicles, and industrial automation.

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Pickl AI

JANUARY 29, 2024

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. Opportunities abound in sectors like healthcare, finance, and automation. billion in 2022 to a remarkable USD 484.17 billion by 2029.

Data Science 52

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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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Pickl AI

APRIL 8, 2024

Summary: AI’s immense potential is undeniable, but its journey riddle with roadblocks. This blog explores 13 major AI blunders, highlighting issues like algorithmic bias, lack of transparency, and job displacement. 13 AI Mistakes That Are Worth Your Attention 1.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

They use self-supervised learning algorithms to perform a variety of natural language processing (NLP) tasks in ways that are similar to how humans use language (see Figure 1). Large language models (LLMs) have taken the field of AI by storm.

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Viso.ai

MARCH 26, 2024

2: Automated Document Analysis and Processing No.3: 4: Algorithmic Trading and Market Analysis No.5: Viso Suite is the Computer Vision Enterprise Platform Computer Vision Algorithms for Finance Models like YOLO (You Only Look Once) models and Faster R-CNN have set benchmarks in real-time processing as well.

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Marktechpost

AUGUST 24, 2023

You will never miss any updates on ML/AI/CV/NLP fields because it is posted on a daily basis and highly moderated to avoid any spam. r/Automate The sub has more than 75k members participating in discussions and posts focused on automation. The subreddit has excellent computer vision and artificial intelligence content.

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Pickl AI

AUGUST 1, 2024

Introduction Deep Learning engineers are specialised professionals who design, develop, and implement Deep Learning models and algorithms. Understanding this role is crucial for anyone interested in pursuing a career in AI and Machine Learning.

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Flipboard

JUNE 20, 2023

Understanding AI’s mysterious “opaque box” is paramount to creating explainable AI. This can be simplified by considering that AI, like all other technology, has a supply chain. When you dissect AI’s supply chain, at the root, you will find algorithms. What factors does it weigh?

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Mlearning.ai

SEPTEMBER 5, 2023

Automated Discovery No more waiting around for manual analysis. InsightsAct’s AI engine works continuously in the background to surface relevant insights. Contextual Recommendations InsightsAct doesn’t just identify issues; its algorithms go further by providing contextual recommendations to address uncovered opportunities.

Business Intelligence 52

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IBM Journey to AI blog

JUNE 28, 2023

Most individual omics informatics tools and algorithms focus on solving a specific problem, which is usually part of a large project. This forces organizations to integrate multiple tools into a single pipeline to serve various goals. gene expression; microbiome data) and any tabular data (e.g.,

Data Analysis 189

Data Analysis Data Scientist Explainable AI AI Strategy 189

Pickl AI

JULY 24, 2024

The pivotal moment in AI’s history occurred with the work of Alan Turing in the 1930s and 1940s. Turing proposed the concept of a “universal machine,” capable of simulating any algorithmic process. During this period, optimism about AI’s potential led to substantial funding and research initiatives.

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How to Learn Machine Learning

JANUARY 21, 2023

Artificial intelligence (AI) is a term that encompasses the use of computer technology to solve complex problems and mimic human decision-making. At its core, AI relies on algorithms, data processing, and machine learning to generate insights from vast amounts of data. billion by 2027, growing at a CAGR of 36.2%.

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Defined.ai blog

AUGUST 10, 2023

From voice assistants like Siri and Alexa, which are now being trained with industry-specific vocabulary and localized dialogue data , to more complex technologies like predictive analytics and autonomous vehicles, AI is everywhere. When it comes to financial data, AI shines brightly. Contact us now to get started.

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Business Intelligence Data Analysis Neural Network AI 52

Viso.ai

DECEMBER 6, 2023

Computer vision (CV) is a rapidly evolving area in artificial intelligence (AI), allowing machines to process complex real-world visual data in different domains like healthcare, transportation, agriculture, and manufacturing. The purpose is to give you an idea of modern computer vision algorithms and applications. Get a demo here.

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Bugra Akyildiz

MAY 25, 2024

Automated Query Optimization: By understanding the underlying data schemas and query patterns, ChatGPT could automatically optimize queries for better performance, indexing recommendations, or distributed execution across multiple data sources. The BPE algorithm is "byte-level" because it runs on UTF-8 encoded strings. Sennrich et al.

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Viso.ai

APRIL 25, 2024

Studies revealed that the error rate for dark-skinned individuals could be 18 times higher than that for light-skinned individuals in some commercial gender classification algorithms. Facial recognition algorithms are one of the areas affected by the sampling bias, as it can cause different error rates depending on the data it was trained on.

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The MLOps Blog

JUNE 27, 2023

For example, if your team works on recommender systems or natural language processing applications, you may want an MLOps tool that has built-in algorithms or templates for these use cases. This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics.

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Dlabs.ai

MAY 22, 2022

It’ll help you get to grips with the fundamentals of ML and its respective algorithms, including linear regression and supervised and unsupervised learning, among others. That’s why it helps to know the fundamentals of ML and the different learning algorithms before you do any data science work.

Artificial Intelligence 52

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Heartbeat

NOVEMBER 27, 2023

Deep learning automates and improves medical picture analysis. Photo by THAVIS 3D on Unsplash Fundamentals of Deep Learning in Medical Image Analysis Deep learning systems automate and accurately analyze complicated imaging data, revolutionizing medical image analysis.

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