IBM Journey to AI blog

JUNE 26, 2023

Generative AI is being analyzed for a variety of use cases including marketing, customer service, retail and education. ChatGPT was the first but today there are many competitors ChatGPT uses a deep learning architecture call the Transformer and represents a significant advancement in the field of NLP.

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Pickl AI

AUGUST 1, 2024

Summary : Deep Learning engineers specialise in designing, developing, and implementing neural networks to solve complex problems. Introduction Deep Learning engineers are specialised professionals who design, develop, and implement Deep Learning models and algorithms.

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Heartbeat

NOVEMBER 27, 2023

Deep learning automates and improves medical picture analysis. Convolutional neural networks (CNNs) can learn complicated patterns and features from enormous datasets, emulating the human visual system. Convolutional Neural Networks (CNNs) Deep learning in medical image analysis relies on CNNs.

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Marktechpost

JUNE 9, 2024

Deep learning methods excel in detecting cardiovascular diseases from ECGs, matching or surpassing the diagnostic performance of healthcare professionals. Explainable AI (xAI) methods, such as saliency maps and attention mechanisms, attempt to clarify these models by highlighting key ECG features.

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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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Unite.AI

JUNE 18, 2024

Looking further ahead, one critical area of focus is Explainable AI , which aims to make AI decisions transparent and understandable. This transparency is necessary to build trust in AI systems and ensure they are used responsibly.

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IBM Journey to AI blog

AUGUST 9, 2024

Most generative AI models start with a foundation model , a type of deep learning model that “learns” to generate statistically probable outputs when prompted.

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Marktechpost

JULY 6, 2023

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. This is why when Explainable AI models can give a clear idea of why a decision was made but not how it arrived at that decision.

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DataRobot Blog

FEBRUARY 14, 2022

DataRobot offers end-to-end explainability to make sure models are transparent at all stages of their lifecycle. In this post, we’ll walk you through DataRobot’s Explainable AI features in both our AutoML and MLOps products and use them to evaluate a model both pre- and post-deployment. Learn More About Explainable AI.

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Unite.AI

SEPTEMBER 20, 2024

Its specialization makes it uniquely adept at powering AI workflows in an industry known for strict regulation and compliance standards. Palmyra-Fin integrates multiple advanced AI technologies, including machine learning, NLP, and deep learning algorithms.

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Marktechpost

JANUARY 19, 2024

Researchers have proposed a deep learning prediction model named XAI-AGE (XAI stands for Explainable AI) that integrates previously identified biologically hierarchical information in a neural network model for predicting the biological age based on DNA methylation data.

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IBM Journey to AI blog

JULY 6, 2023

Python is the most common programming language used in machine learning. Machine learning and deep learning are both subsets of AI. Deep learning teaches computers to process data the way the human brain does. Deep learning algorithms are neural networks modeled after the human brain.

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IBM Journey to AI blog

AUGUST 11, 2023

MLOps is the next evolution of data analysis and deep learning. Simply put, MLOps uses machine learning to make machine learning more efficient. Generative AI is a type of deep-learning model that takes raw data, processes it and “learns” to generate probable outputs.

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Marktechpost

JULY 22, 2024

With deep learning models like BERT and RoBERTa, the field has seen a paradigm shift. Existing methods for AV have advanced significantly with the use of deep learning models. This is a critical limitation as the demand for explainable AI grows.

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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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IBM Journey to AI blog

JULY 17, 2023

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. Enterprise organizations (many of whom have already embarked on their AI journeys) are eager to harness the power of generative AI for customer service.

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Unite.AI

JANUARY 12, 2024

The Evolution of AI Research As capabilities have grown, research trends and priorities have also shifted, often corresponding with technological milestones. The rise of deep learning reignited interest in neural networks, while natural language processing surged with ChatGPT-level models.

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Towards AI

FEBRUARY 22, 2024

If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Mh_aghajany is looking for fellow learners to explore Machine Learning, Deep Learning, and LLM. Our friends at Zoī are hiring their Chief AI Officer.

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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. CorgiAI CorgiAI is a fraud detection and prevention tool designed to increase income and reduce losses due to fraud.

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AWS Machine Learning Blog

APRIL 12, 2023

Games24x7 employs an automated, data-driven, AI powered framework for the assessment of each player’s behavior through interactions on the platform and flags users with anomalous behavior. He is currently involved in research efforts in the area of explainable AI and deep learning.

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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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NVIDIA

DECEMBER 13, 2023

Financial Services Firms Embrace AI for Identity Verification The financial services industry is developing AI for identity verification. Tackling Model Explainability and Bias GNNs also enable model explainability with a suite of tools.

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ODSC - Open Data Science

FEBRUARY 13, 2023

Well according to our research, there is quite a lot going on in the world of machine learning safety and security. Explainable AI: As in the namesake, explainable AI’s purpose is to clearly and in a transparent manner, explain why a machine learning model came to a specific decision.

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Marktechpost

AUGUST 24, 2023

r/neuralnetworks The Subreddit is about Deep Learning, Artificial Neural Networks, and Machine Learning. members and is a great place to learn more about the latest AI. It has over 37.4k members and has active discussions on various ML topics. It features various posts and regular discussions on these topics.

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Pickl AI

SEPTEMBER 3, 2024

ReLU is widely used in Deep Learning due to its simplicity and effectiveness in mitigating the vanishing gradient problem. Tanh (Hyperbolic Tangent): This function maps input values to a range between -1 and 1, providing a smooth gradient for learning.

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ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Advanced ML Algorithms & EnsembleMethods Hyperparameter Tuning & Model Optimization AutoML & Real-Time MLSystems Explainable AI & EthicalAI Time Series Forecasting & NLP Techniques Who Should Attend: ML Engineers, Data Scientists, and Technical Practitioners working on production-level ML solutions.

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Marktechpost

JULY 19, 2023

However, another associated phenomenon that poses a danger to the effectiveness of human-AI decision-making teams is AI overreliance, which establishes that people are influenced by AI and often accept incorrect decisions without verifying whether the AI is correct. Check out the Paper and Stanford Article.

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Heartbeat

SEPTEMBER 13, 2023

Through the explainability of AI systems, it becomes easier to build trust, ensure accountability, and enable humans to comprehend and validate the decisions made by these models. For example, explainability is crucial if a healthcare professional uses a deep learning model for medical diagnoses. Russell, C. &

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The MLOps Blog

JUNE 27, 2023

With extensive language support and integration with major deep learning frameworks, the Model Hub simplifies the integration of pre-trained models and libraries into existing workflows, making it a valuable resource for researchers, developers, and data scientists. Monitor the performance of machine learning models.

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Unite.AI

JUNE 24, 2024

Because mathematicians tend to favor elegant solutions over complex machinery, I’ve always tried to emphasize simplicity when applying machine learning to business problems. What are some future trends in AI and data science that you are excited about, and how is Astronomer preparing for them?

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ODSC - Open Data Science

FEBRUARY 18, 2025

AI Explainability Specialists: As AI models become increasingly complex, understanding their decision-making processes is crucial. AI explainability specialists develop techniques and tools to interpret and explain AI outputs, fostering trust and transparency.

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Pickl AI

SEPTEMBER 17, 2024

What Is the Difference Between Artificial Intelligence, Machine Learning, And Deep Learning? Artificial Intelligence (AI) is a broad field that encompasses the development of systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.

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ODSC - Open Data Science

DECEMBER 17, 2024

For example, AI-based lending tools could disproportionately deny loans to minority groups , even if unintentionally. Many AI models, especially deep learning systems, are black boxes with opaque decision-making processes. Transparency is another issue.

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Pickl AI

JULY 14, 2023

Big Data and Deep Learning (2010s-2020s): The availability of massive amounts of data and increased computational power led to the rise of Big Data analytics. Deep Learning, a subfield of ML, gained attention with the development of deep neural networks.

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Pickl AI

SEPTEMBER 17, 2024

Some of the key future trends include: Increased Use of Deep Learning and Neural Networks As computing power and data availability continue to grow, we can expect to see more advanced Deep Learning models being applied to cybersecurity challenges, enabling even more accurate threat detection and prediction.

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Viso.ai

DECEMBER 6, 2023

Real-Time Computer Vision: With the help of advanced AI hardware , computer vision solutions can analyze real-time video feeds to provide critical insights. The most common example is security analytics , where deep learning models analyze CCTV footage to detect theft, traffic violations, or intrusions in real-time.

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Pickl AI

JULY 24, 2024

AI in the 21st Century The 21st century has witnessed an unprecedented boom in AI research and applications. The advent of big data, coupled with advancements in Machine Learning and deep learning, has transformed the landscape of AI. 2011: IBM Watson defeats Ken Jennings on the quiz show “Jeopardy!

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