NYU Center for Data Science

JULY 5, 2024

Our study demonstrates that machine supervision significantly improves two crucial medical imaging tasks: classification and segmentation,” said Cirrone, who leads AI efforts at the Colton Center for Autoimmunity at NYU Langone. “The

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NYU Center for Data Science

NOVEMBER 3, 2023

Self-supervised learning (SSL) has emerged as a powerful technique for training deep neural networks without extensive labeled data. However, unlike supervised learning, where labels help identify relevant information, the optimal SSL representation heavily depends on assumptions made about the input data and desired downstream task.

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IBM Data Science in Practice

MARCH 17, 2025

Currently, hand-crafted compression algorithms, often designed for general image data like JPEG2000, are applied. On March 10, 2025, the Embed2Scale consortium launched the 2025 CVPR EARTHVISION Data Challenge , inviting researchers and AI practitioners to develop innovative EO data compression techniques.

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Towards AI

APRIL 24, 2025

Last Updated on April 24, 2025 by Editorial Team Author(s): SETIA BUDI SUMANDRA Originally published on Towards AI. Thats the motto of Unsupervised Learning a fascinating branch of machine learning where algorithms learn patterns from unlabeled data. Join thousands of data leaders on the AI newsletter.

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AWS Machine Learning Blog

JANUARY 31, 2025

Increasingly, FMs are completing tasks that were previously solved by supervised learning, which is a subset of machine learning (ML) that involves training algorithms using a labeled dataset. George Lee is AVP, Data Science & Generative AI Lead for International at Travelers Insurance.

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Data Science Dojo

JULY 2, 2024

Adaptive AI has risen as a transformational technological concept over the years, leading Gartner to name it as a top strategic tech trend for 2023. It is a step ahead within the realm of artificial intelligence (AI). As the use of AI has expanded into various arenas of the world, the technology has also developed over time.

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Data Science Dojo

MAY 10, 2024

Their impact on ML tasks has made them a cornerstone of AI advancements. Unsupervised vs. supervised learning for embeddings While vector representation and contextual inference remain important factors in the evolution of LLM embeddings, the lens of comparative analysis also highlights another aspect for discussion.

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Dataconomy

MAY 29, 2023

In the field of AI and ML, QR codes are incredibly helpful for improving predictive analytics and gaining insightful knowledge from massive data sets. These algorithms allow AI systems to recognize patterns, forecast outcomes, and adjust to new situations.

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Dataconomy

APRIL 17, 2025

Image-to-image translation is a fascinating area of generative AI that harnesses advanced algorithms to transform existing images into new forms while retaining essential characteristics. Understanding generative AI Generative AI encompasses a range of algorithms designed to create new content based on pre-existing data.

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Dataconomy

SEPTEMBER 13, 2023

AI annotation jobs are on the rise; naturally, people started asking what exactly is data annotation. AI annotation jobs: What is data annotation? AI still needs a human hand to operate efficiently; for how long, though? These tasks are indispensable, as algorithms heavily rely on pattern recognition to make informed decisions.

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Pickl AI

APRIL 3, 2023

Accordingly, Machine Learning allows computers to learn and act like humans by providing data. Apparently, ML algorithms ensure to train of the data enabling the new data input to make compelling predictions and deliver accurate results. Therefore, Supervised Learning vs Unsupervised Learning is part of Machine Learning.

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NYU Center for Data Science

SEPTEMBER 13, 2024

The world of multi-view self-supervised learning (SSL) can be loosely grouped into four families of methods: contrastive learning, clustering, distillation/momentum, and redundancy reduction. I don’t think it will replace existing algorithms,” Shwartz-Ziv noted.

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Google Research AI blog

FEBRUARY 10, 2023

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Hence, developing algorithms with improved efficiency, performance and speed remains a high priority as it empowers services ranging from Search and Ads to Maps and YouTube. You can find other posts in the series here.)

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Dataconomy

APRIL 24, 2025

Unsupervised learning is a fascinating area within machine learning that uncovers hidden patterns in data without the need for pre-labeled examples. By allowing algorithms to learn autonomously, it opens the door to various innovative applications across different fields. What is unsupervised learning?

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Towards AI

JULY 3, 2024

Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. When it comes to the three best algorithms to use for spatial analysis, the debate is never-ending. Although practitioners’ tastes may differ, several algorithms are regularly preferred because of their strength, adaptability, and efficiency.

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Towards AI

APRIL 8, 2024

Last Updated on April 8, 2024 by Editorial Team Author(s): Eashan Mahajan Originally published on Towards AI. Photo by Arseny Togulev on Unsplash With machine learning’s surge of popularity in the past few years, more and more people spend hours each day trying to learn as much as they can. Let’s get right into it.

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Dataconomy

MAY 25, 2023

On the other hand, artificial intelligence is the simulation of human intelligence in machines that are programmed to think and learn like humans. By leveraging advanced algorithms and machine learning techniques, IoT devices can analyze and interpret data in real-time, enabling them to make informed decisions and take autonomous actions.

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Data Science Dojo

JULY 4, 2024

Generative AI applications like ChatGPT and Gemini are becoming indispensable in today’s world. What is Reinforcement Learning from Human Feedback Reinforcement Learning from Human Feedback is a cutting-edge machine learning technique used to enhance the performance and reliability of AI models.

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Pickl AI

FEBRUARY 17, 2025

This scenario highlights a common reality in the Machine Learning landscape: despite the hype surrounding ML capabilities, many projects fail to deliver expected results due to various challenges. Statistics reveal that 81% of companies struggle with AI-related issues ranging from technical obstacles to economic concerns.

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Towards AI

SEPTEMBER 3, 2024

Last Updated on September 3, 2024 by Editorial Team Author(s): Surya Maddula Originally published on Towards AI. Let’s discuss two popular ML algorithms, KNNs and K-Means. They are both ML Algorithms, and we’ll explore them more in detail in a bit. Join thousands of data leaders on the AI newsletter.

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Data Science Dojo

MAY 10, 2024

Their impact on ML tasks has made them a cornerstone of AI advancements. Read on to understand the role of embeddings in generative AI Let’s take a step back and travel through the journey of LLM embeddings from the start to the present day, understanding their evolution every step of the way.

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Dataconomy

APRIL 18, 2023

With the rise of AI-generated art and AI-powered chatbots like ChatGPT, it’s clear that artificial intelligence has become a ubiquitous part of our daily lives. These cutting-edge technologies have captured the public imagination, fueling speculation about the future of AI and its impact on society.

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Towards AI

JANUARY 11, 2024

Last Updated on January 12, 2024 by Editorial Team Author(s): Davide Nardini Originally published on Towards AI. Arguably, one of the most important concepts in machine learning is classification. This article will illustrate the difference between classification and regression in machine learning. Published via Towards AI

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Dataconomy

OCTOBER 5, 2023

If you are interested in technology at all, it is hard not to be fascinated by AI technologies. Whether it’s pushing the limits of creativity with its generative abilities or knowing our needs better than us with its advanced analysis capabilities, many sectors have already taken a slice of the huge AI pie.

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ML @ CMU

APRIL 23, 2025

Interpretability and Explainable AI Learning on Graphs and Other Geometries & Topologies Learning Theory Neurosymbolic & Hybrid AI Systems (Physics-Informed, Logic & Formal Reasoning, etc.) Optimization Other Topics in Machine Learning (i.e.,

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Dataconomy

AUGUST 31, 2023

Machine learning models have already started to take up a lot of space in our lives, even if we are not consciously aware of it. Embracing AI systems and technology day by day, humanity is experiencing perhaps the fastest development in recent years. You want an example: ChatGPT, Alexa, autonomous vehicles and many more on the way.

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IBM Journey to AI blog

DECEMBER 20, 2023

That’s why diversifying enterprise AI and ML usage can prove invaluable to maintaining a competitive edge. Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. What is machine learning? Here, we’ll discuss the five major types and their applications.

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Google Research AI blog

MARCH 6, 2023

First, there is a lack of scalability with conventional supervised learning approaches. In contrast, self-supervised learning can leverage audio-only data, which is available in much larger quantities across languages. This requires the learning algorithm to be flexible, efficient, and generalizable.

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AWS Machine Learning Blog

JANUARY 31, 2025

By applying AI to these digitized WSIs, researchers are working to unlock new insights and enhance current annotations workflows. These models are trained using self-supervised learning algorithms on expansive datasets, enabling them to capture a comprehensive repertoire of visual representations and patterns inherent within pathology images.

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AWS Machine Learning Blog

FEBRUARY 13, 2025

Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case. This is the k-nearest neighbor (k-NN) algorithm. The following figure illustrates how this works.

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Dataconomy

MAY 4, 2023

From predicting disease outbreaks to identifying complex medical patterns and helping researchers develop targeted therapies, the potential applications of machine learning in healthcare are vast and varied. What is machine learning? From personalized medicine to disease prevention, the possibilities are endless.

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Towards AI

APRIL 7, 2024

Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst?

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Dataconomy

MAY 22, 2023

By dividing the workload and data across multiple nodes, distributed learning enables parallel processing, leading to faster and more efficient training of machine learning models. There are various types of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning.

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Smart Data Collective

JUNE 4, 2021

Some of the applications of data science are driverless cars, gaming AI, movie recommendations, and shopping recommendations. Data scientists use algorithms for creating data models. Whereas in machine learning, the algorithm understands the data and creates the logic. In supervised learning, a variable is predicted.

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Dataconomy

AUGUST 28, 2023

The concept of a kernel in machine learning might initially sound perplexing, but it’s a fundamental idea that underlies many powerful algorithms. Kernels in machine learning serve as a bridge between linear and nonlinear transformations. So how can you use kernel in machine learning for your own algorithm?

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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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Becoming Human

MARCH 16, 2023

AI drug discovery is exploding. Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. AI has already helped identify promising candidate therapeutics, and it didn’t take years but months or even days. We will look at success stories, AI benefits, and limitations.

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