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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Artificial Intelligence Artificial Intelligence Robotics Machine Learning 132

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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Artificial Intelligence Artificial Intelligence Explainable AI AI 98

How to Learn Machine Learning

JUNE 9, 2024

The thought of machine learning and AI will definitely pop into your mind when the conversation is about emerging technologies. Healthcare organizations are using healthcare AI/ML solutions to achieve operational efficiency and deliver quality patient care. Explainability AI addresses these challenges of AI/ML solutions.

Machine Learning 52

Machine Learning Explainable AI Explainability ML 52

Pickl AI

NOVEMBER 5, 2024

These statistics underscore the significant impact that Data Science and AI are having on our future, reshaping how we analyse data, make decisions, and interact with technology. Lack of Transparency Many AI systems operate as “black boxes,” making it difficult for users to understand how decisions are made.

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Data Science Artificial Intelligence Artificial Intelligence Machine Learning 52

IBM Journey to AI blog

JUNE 28, 2023

With Amazon Omics awareness of file formats like FASTQ, BAM and CRAM, clients can focus on data, bring in workflow definition tools like WDL, letting Amazon Omics take care of the rest. This process alone saves hundreds of hours of productive time. gene expression; microbiome data) and any tabular data (e.g.,

Data Analysis 189

Data Analysis Data Scientist Explainable AI AI Strategy 189

AWS Machine Learning Blog

NOVEMBER 15, 2024

At AWS, we are committed to developing AI responsibly , taking a people-centric approach that prioritizes education, science, and our customers, integrating responsible AI across the end-to-end AI lifecycle. What constitutes responsible AI is continually evolving. This is a powerful method to reduce hallucinations.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

IBM watsonx.data is a fit-for-purpose data store built on an open lakehouse architecture to scale AI workloads for all of your data, anywhere. IBM watsonx.governance is an end-to-end automated AI lifecycle governance toolkit that is built to enable responsible, transparent and explainable AI workflows.

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Large Language Models DevOps AI Modeling Automation 240

Ofemwire

MARCH 1, 2024

At its core, AI is designed to replicate or even surpass human cognitive functions, employing algorithms and machine learning to interpret complex data, make decisions, and execute tasks with unprecedented speed and accuracy. If you dont get that, let me explain what AI is, like I would do to a fifth grader.

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Neural Network Artificial Intelligence Artificial Intelligence Machine Learning 52

Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. Explainable AI (XAI) The demand for transparency in Machine Learning Models is growing. Explainable AI (XAI) focuses on making complex models more interpretable to humans. Data quality significantly impacts model performance.

Machine Learning 52

Machine Learning Algorithm Data Quality Neural Network 52

AWS Machine Learning Blog

APRIL 12, 2023

Because we wanted to track the metrics of an ongoing training job and compare them with previous training jobs, we just had to parse this StdOut by defining the metric definitions through regex to fetch the metrics from StdOut for every epoch. He is currently involved in research efforts in the area of explainable AI and deep learning.

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Metadata Deep Learning ML Data Science 104

Pickl AI

JANUARY 29, 2024

AI automates and optimises Data Science workflows, expediting analysis for strategic decision-making. Data Science Vs Machine Learning Vs AI Aspect Data Science Artificial Intelligence Machine Learning Definition Data Science is the field that deals with the extraction of knowledge and insights from data through various processes.

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Data Science ML Machine Learning Artificial Intelligence 52

DataRobot Blog

JUNE 23, 2022

The explicit management of both ensures compliance (especially when transparent and explainable AI models are used), and the business ownership necessary to create business value. With a clear definition of the decision-making approach, decisions made can be logged. Take Action.

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AI AI Data Science AI Modeling 59

The MLOps Blog

AUGUST 11, 2023

But some of these queries are still recurrent and haven’t been explained well. Here, the DAGs represent workflows comprising units embodying job definitions for operations to be carried out, known as Steps. How should the machine learning pipeline operate?

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AWS Machine Learning Blog

NOVEMBER 9, 2023

Under Advanced Project Options , for Definition , select Pipeline script from SCM. She is passionate about developing, deploying, and explaining AI/ ML solutions across various domains. Select This project is parameterized. On the Add Parameter menu, choose String Parameter. For Name , enter prodAccount. For SCM , choose Git.

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Data Drift Auto-complete ML Automation 125

Mlearning.ai

MARCH 24, 2023

The EU AI Act is a proposed piece of legislation that seeks to regulate the development and deployment of artificial intelligence (AI) systems across the European Union. Photo by Guillaume Périgois on Unsplash EU AI Act: History and Timeline 2018 : EU Commission starts pilot project on ‘Explainable AI’.

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Artificial Intelligence Artificial Intelligence Categorization Explainability 52

DataRobot Blog

JUNE 24, 2022

In this example, DataRobot’s AI Cloud Platform is used to forecast the next year’s house price at each submarket (e.g., Forecasting Prime House Prices. district or neighborhood level), clustered into different markets (e.g., city level).

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Machine Learning AI AI Automation 52