Google Research AI blog

JUNE 18, 2023

Posted by Shaina Mehta, Program Manager, Google This week marks the beginning of the premier annual Computer Vision and Pattern Recognition conference (CVPR 2023), held in-person in Vancouver, BC (with additional virtual content).

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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 form of AI that learns, adapts, and improves as it encounters changes, both in data and the environment. It is a step ahead within the realm of artificial intelligence (AI).

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NYU Center for Data Science

NOVEMBER 15, 2023

We’re excited to announce that many CDS faculty, researchers, and students will present at the upcoming thirty-seventh 2023 NeurIPS (Neural Information Processing Systems) Conference , taking place Sunday, December 10 through Saturday, December 16. The conference will take place in-person at the New Orleans Ernest N.

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Towards AI

JULY 22, 2023

Last Updated on July 24, 2023 by Editorial Team Author(s): Cristian Originally published on Towards AI. In the context of Machine Learning, data can be anything from images, text, numbers, to anything else that the computer can process and learn from. Instead, it learns by finding patterns and structures in the input data.

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Google Research AI blog

JULY 23, 2023

We build ML systems to solve deep scientific and engineering challenges in areas of language, music, visual processing, algorithm development, and more. Google is proud to be a Diamond Sponsor of the 40th International Conference on Machine Learning (ICML 2023), a premier annual conference, which is being held this week in Honolulu, Hawaii.

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ODSC - Open Data Science

AUGUST 29, 2023

As industries begin to scale and learn how to fully utilize the power of AI, it’s likely that more and more machine learning engineers will work closely to further refine prompt strategies, curbing biases and advancing human-AI conversations.

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Snorkel AI

OCTOBER 31, 2023

The Snorkel AI team will present 18 research papers and talks at the 2023 Neural Information Processing Systems (NeurIPS) conference from December 10-16. The Snorkel papers cover a broad range of topics including fairness, semi-supervised learning, large language models (LLMs), and domain-specific models.

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Pickl AI

FEBRUARY 2, 2025

Mn in 2023, with an estimated CAGR of 11.8%, the importance of such techniques continues to rise. Classification: How it Differs from Association Rules Classification is a supervised learning technique that aims to predict a target or class label based on input features. The rules are then applied for classification purposes.

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BAIR

JULY 10, 2023

Figure 1: stepwise behavior in self-supervised learning. When training common SSL algorithms, we find that the loss descends in a stepwise fashion (top left) and the learned embeddings iteratively increase in dimensionality (bottom left). and “how does that learning actually occur?” lack basic answers.

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Google Research AI blog

MAY 25, 2023

Chirp is able to cover such a wide variety of languages by leveraging self-supervised learning on unlabeled multilingual dataset with fine-tuning on a smaller set of labeled data. Learn more about the fingerspelling Kaggle competition , which features a song from Sean Forbes , a deaf musician and rapper.

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Becoming Human

MAY 15, 2023

The two most common types of supervised learning are classification , where the algorithm predicts a categorical label, and regression , where the algorithm predicts a numerical value. Unsupervised Learning In this type of learning, the algorithm is trained on an unlabeled dataset, where no correct output is provided.

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Snorkel AI

OCTOBER 31, 2023

The Snorkel AI team will present 18 research papers and talks at the 2023 Neural Information Processing Systems (NeurIPS) conference from December 10-16. The Snorkel papers cover a broad range of topics including fairness, semi-supervised learning, large language models (LLMs), and domain-specific models.

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Towards AI

APRIL 18, 2023

Last Updated on April 21, 2023 by Editorial Team Author(s): Sriram Parthasarathy Originally published on Towards AI. Building disruptive Computer Vision applications with No Fine-Tuning Imagine a world where computer vision models could learn from any set of images without relying on labels or fine-tuning. Sounds futuristic, right?

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ODSC - Open Data Science

MARCH 22, 2023

Cleanlab is an open-source software library that helps make this process more efficient (via novel algorithms that automatically detect certain issues in data) and systematic (with better coverage to detect different types of issues). Data-centric AI instead asks how we can systematically engineer better data through algorithms/automation.

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AWS Machine Learning Blog

SEPTEMBER 10, 2024

The final phase improved on the results of HEEC and PORPOISE—both of which have been trained in a supervised fashion—using a foundation model trained in a self-supervised manner, namely Hierarchical Image Pyramid Transformer (HIPT) ( Chen et al., 2023 ), has been investigated in the final stage of the PoC exercises.

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Towards AI

JULY 22, 2023

Last Updated on July 24, 2023 by Editorial Team Author(s): Muhammad Arham Originally published on Towards AI. The math behind the Logistic Regression algorithm and implementation from scratch using Numpy. Therefore, Logistic Regression is a binary classification algorithm. The class labels, denoted by y, are either 0 or 1.

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DrivenData Labs

JUNE 14, 2023

Between December 2022 and April 2023, 404 participants from 59 countries signed up to solve the problems posed by the two tracks, and 82 went on to submit solutions. Accurate and performant algorithms are critical in flagging and removing inappropriate content. two videos edited so they were side-by-side vertically or horizontally).

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Towards AI

JULY 23, 2023

Last Updated on July 25, 2023 by Editorial Team Author(s): Abhijit Roy Originally published on Towards AI. Semi-Supervised Sequence Learning As we all know, supervised learning has a drawback, as it requires a huge labeled dataset to train. But, the question is, how did all these concepts come together?

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BAIR

APRIL 6, 2023

The closest analogue in academia is interactive imitation learning (IIL) , a paradigm in which a robot intermittently cedes control to a human supervisor and learns from these interventions over time. Using this formalism, we can now instantiate and compare IFL algorithms (i.e., allocation policies) in a principled way.

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Snorkel AI

SEPTEMBER 29, 2023

Some machine learning algorithms, such as clustering and self-supervised learning , do not require data labels, but their direct business applications are limited. Use cases for supervised machine learning models, on the other hand, cover many business needs.

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Becoming Human

MARCH 16, 2023

AI began back in the 1950s as a simple series of “if, then rules” and made its way into healthcare two decades later after more complex algorithms were developed. Since the advent of deep learning in the 2000s, AI applications in healthcare have expanded. Originally published at [link] on January 27, 2023. Drop us a line.

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Hacker News

MAY 3, 2023

With these fairly complex algorithms often being described as “giant black boxes” in news and media, a demand for clear and accessible resources is surging. Fine-tuning may involve further training the pre-trained model on a smaller, task-specific labeled dataset, using supervised learning.

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ODSC - Open Data Science

SEPTEMBER 15, 2023

With its advanced algorithms and language comprehension, it can navigate complex datasets and distill valuable insights. This synthetic data serves as a viable alternative for training models, testing algorithms, and ensuring privacy compliance. Now, are you ready to learn more?

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Mlearning.ai

SEPTEMBER 24, 2023

A brute-force search is a general problem-solving technique and algorithm paradigm. Maximum Time by the algorithm The running time complexity (Big O notation) is different for different algorithms. Big O notation is a mathematical concept to describe the complexity of algorithms. Johnston, B. and Mathur, I.

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Towards AI

MARCH 1, 2023

Last Updated on March 4, 2023 by Editorial Team Author(s): Towards AI Editorial Team Originally published on Towards AI. The class covers algorithms for finding and fixing common issues in ML data, as well as constructing better datasets, with a concentration on data used in supervised learning tasks such as classification.

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Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. billion in 2023 to $181.15

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Snorkel AI

MARCH 1, 2023

Foundation models are large AI models trained on enormous quantities of unlabeled data—usually through self-supervised learning. What is self-supervised learning? Self-supervised learning is a kind of machine learning that creates labels directly from the input data. Find out in the guide below.

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ODSC - Open Data Science

OCTOBER 2, 2023

It can also be used to generate code for specific purposes, such as generating code to implement a specific algorithm or to generate code to solve a specific problem. The best place to do this is at ODSC West 2023 this October 30th to November 2nd. Freeing up valuable time for developers to focus on more complex projects and planning.

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DrivenData Labs

FEBRUARY 3, 2025

I generated unlabeled data for semi-supervised learning with Deberta-v3, then the Deberta-v3-large model was used to predict soft labels for the unlabeled data. The semi-supervised learning was repeated using the gemma2-9b model as the soft labeling model. Then we leveraged the benefits of NLP algorithms (e.g.,

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Pickl AI

OCTOBER 24, 2024

Meanwhile, the ML market , valued at $48 billion in 2023, is expected to hit $505 billion by 2031. Key Takeaways Scope and Purpose : Artificial Intelligence encompasses a broad range of technologies to mimic human intelligence, while Machine Learning focuses explicitly on algorithms that enable systems to learn from data.

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Mlearning.ai

AUGUST 20, 2023

Photo by Bruno Nascimento on Unsplash Introduction Data is the lifeblood of Machine Learning Models. Before we feed data into a learning algorithm, we need to make sure that we pre-process the data. Many Machine Learning algorithms don’t work with missing data. Available at: [link] (Accessed: 25 March 2023).

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Pickl AI

NOVEMBER 13, 2024

This process ensures that networks learn from data and improve over time. billion in 2023 to an estimated USD 311.13 Understanding Backpropagation Backpropagation, short for “backward propagation of errors,” is a core algorithm for training artificial neural networks. As the neural network software market grows from USD 23.10

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Dataconomy

APRIL 4, 2023

Artificial Intelligence (AI) models are the building blocks of modern machine learning algorithms that enable machines to learn and perform complex tasks. These models are designed to replicate the human brain’s cognitive functions, enabling them to perceive, reason, learn, and make decisions based on data.

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Dataconomy

APRIL 4, 2023

Artificial Intelligence (AI) models are the building blocks of modern machine learning algorithms that enable machines to learn and perform complex tasks. These models are designed to replicate the human brain’s cognitive functions, enabling them to perceive, reason, learn, and make decisions based on data.

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ODSC - Open Data Science

FEBRUARY 14, 2023

The model was fine-tuned to reduce false, harmful, or biased output using a combination of supervised learning in conjunction to what OpenAI calls Reinforcement Learning with Human Feedback (RLHF), where humans rank potential outputs and a reinforcement learning algorithm rewards the model for generating outputs like those that rank highly.

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Mlearning.ai

AUGUST 8, 2023

Background Many of the new exciting AI breakthroughs have come from two recent innovations: self-supervised learning and Transformers. The student network is encouraged to learn more information representations by predicting the output of a teacher network which has a more complex architecture. What is Grounding DINO?

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Mlearning.ai

APRIL 16, 2023

It may seem simple linear regression is neglected in the machine learning world of today. It helps to understand higher and more complex algorithms. So, it is important to master this algorithm. In this tutorial, you will learn about the concepts behind simple linear regression. 2019) Python Machine Learning.

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