Google Research AI blog

FEBRUARY 10, 2023

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Google Research has been at the forefront of this effort, developing many innovations from privacy-safe recommendation systems to scalable solutions for large-scale ML. You can find other posts in the series here.)

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Chatbots Life

APRIL 19, 2024

Generative AI Overview According to McKinsey , Generative AI is “a type of AI that can create new data (text, code, images, video) using patterns it has learned by training on extensive (public) data with machine learning (ML) techniques.” These include unsupervised or semi-supervised learning.

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

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Snorkel AI

FEBRUARY 6, 2023

  Anirudh Koul is Machine Learning Lead for the NASA Frontier Development Lab and the Head of Machine Learning Sciences at Pinterest. He presented at Snorkel AI’s 2022 Future of Data Centric AI (FDCAI) Conference. And that’s the power of self-supervised learning. A transcript of Koul’s talk is provided below.

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Snorkel AI

FEBRUARY 6, 2023

Anirudh Koul is Machine Learning Lead for the NASA Frontier Development Lab and the Head of Machine Learning Sciences at Pinterest. He presented at Snorkel AI’s 2022 Future of Data Centric AI (FDCAI) Conference. And that’s the power of self-supervised learning. A transcript of Koul’s talk is provided below.

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DrivenData Labs

JUNE 14, 2023

In December 2022, DrivenData and Meta AI launched the Video Similarity Challenge. 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. student in ReLER, University of Technology Sydney, supervised by Yi Yang.

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Google Research AI blog

JANUARY 18, 2023

Please keep your eye on this space and look for the title “Google Research, 2022 & Beyond” for more articles in the series. With this post, I am kicking off a series in which researchers across Google will highlight some exciting progress we've made in 2022 and present our vision for 2023 and beyond.

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BAIR

APRIL 29, 2022

At the same time as the emergence of powerful RL systems in the real world, the public and researchers are expressing an increased appetite for fair, aligned, and safe machine learning systems. However the unique ability of RL systems to leverage temporal feedback in learning complicates the types of risks and safety concerns that can arise.

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Google Research AI blog

NOVEMBER 28, 2022

Posted by Cat Armato, Program Manager, Google This week marks the beginning of the 36th annual Conference on Neural Information Processing Systems ( NeurIPS 2022 ), the biggest machine learning conference of the year.

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Heartbeat

NOVEMBER 16, 2023

The process involves supervised learning (SL) and reinforcement learning (RL) phases. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

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Snorkel AI

JULY 5, 2023

Abhishek Ratna, in AI ML marketing, and TensorFlow developer engineer Robert Crowe, both from Google, spoke as part of a panel entitled “Practical Paths to Data-Centricity in Applied AI” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. And in supervised learning, it has to be labeled data.

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Snorkel AI

JULY 5, 2023

Abhishek Ratna, in AI ML marketing, and TensorFlow developer engineer Robert Crowe, both from Google, spoke as part of a panel entitled “Practical Paths to Data-Centricity in Applied AI” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. And in supervised learning, it has to be labeled data.

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Snorkel AI

MARCH 30, 2023

Rapid, model-guided iteration with New Studio for all core ML tasks. Enhanced studio experience for all core ML tasks. Snorkel introduced Data-centric Foundation Model Development capabilities in November 2022 for enterprises to overcome these challenges and leverage foundation models in production. PDF extraction improvements.

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Dataconomy

APRIL 4, 2023

Reminder : Training data refers to the data used to train an AI model, and commonly there are three techniques for it: Supervised learning: The AI model learns from labeled data, which means that each data point has a known output or target value. In March of 2022, DeepMind released Chinchilla AI.

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Dataconomy

APRIL 4, 2023

Reminder : Training data refers to the data used to train an AI model, and commonly there are three techniques for it: Supervised learning: The AI model learns from labeled data, which means that each data point has a known output or target value. In March of 2022, DeepMind released Chinchilla AI.

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Becoming Human

MARCH 16, 2023

Since the advent of deep learning in the 2000s, AI applications in healthcare have expanded. Machine Learning Machine learning (ML) focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed. A few AI technologies are empowering drug design.

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Explosion

MAY 17, 2023

Once you’re past prototyping and want to deliver the best system you can, supervised learning will often give you better efficiency, accuracy and reliability than in-context learning for non-generative tasks — tasks where there is a specific right answer that you want the model to find. That’s not a path to improvement.

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Heartbeat

FEBRUARY 28, 2023

U-Net , U-Net++ ], whereas unsupervised learning eliminates this requirement [see this r eview paper ]. Semi-supervised learning lies in between supervised and unsupervised learning, which we will learn in detail in the following sections. What is Semi-supervised Learning (SSL)?

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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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Snorkel AI

APRIL 18, 2023

Prasanna Balaprakash, research and development lead from Argonne National Laboratory gave a presentation entitled “Extracting the Impact of Climate Change from Scientific Literature using Snorkel-Enabled NLP” at Snorkel AI’s Future of Data-Centric AI Workshop in August, 2022. One way that we did is using weak supervised learning.

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Snorkel AI

APRIL 18, 2023

Prasanna Balaprakash, research and development lead from Argonne National Laboratory gave a presentation entitled “Extracting the Impact of Climate Change from Scientific Literature using Snorkel-Enabled NLP” at Snorkel AI’s Future of Data-Centric AI Workshop in August, 2022. One way that we did is using weak supervised learning.

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Snorkel AI

JUNE 27, 2023

Chip Huyen, co-founder and CEO of Claypot AI gave a presentation entitled “Platform for Real-Time Machine Learning” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. I’m a co-founder of Claypot AI, and I assist with machine learning AI systems designed at Stanford. Hello, my name is Chip.

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Snorkel AI

JUNE 27, 2023

Chip Huyen, co-founder and CEO of Claypot AI gave a presentation entitled “Platform for Real-Time Machine Learning” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. I’m a co-founder of Claypot AI, and I assist with machine learning AI systems designed at Stanford. Hello, my name is Chip.

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ML @ CMU

SEPTEMBER 15, 2023

(ii) We showcase the effectiveness of SSL-based TTA approaches for scene decomposition, while previous self-supervised test-time adaptation methods have primarily demonstrated results in classification tasks. 2021) with test time adaptation using BYOL self-supervised loss of MT3 (Bartler et al.

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DataRobot Blog

DECEMBER 15, 2022

supervised learning and time series regression). Note: the DataRobot platform supports both supervised and unsupervised learning. Configuring an ML project. To begin training your model, just hit the Start button and let the DataRobot platform train ML models for you. AI Experience 2022 Recordings.

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Mlearning.ai

SEPTEMBER 4, 2023

International Conference on Learning Representations. [20] 20] Once you have your instruction data, you split it into training, validation, and test sets, like in standard supervised learning. The researchers adopt the style from Jordan Hoffmann et al. “An An empirical analysis of compute-optimal large language model training.”

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Mlearning.ai

JULY 31, 2023

Data2Vec: A General Framework For Self-Supervised Learning in Speech, Vision and Language. Check out my introductory article here that acts as a roadmap through the architecture. References Baevski, A., and Auli, M., Available from: [link]. Bojanowski, P., and Mikolov, T., Enriching Word Vectors with Subword Information. arXiv.org.

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Mlearning.ai

APRIL 13, 2023

Before we discuss the above related to kernels in machine learning, let’s first go over a few basic concepts: Support Vector Machine , S upport Vectors and Linearly vs. Non-linearly Separable Data. Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis.

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Snorkel AI

FEBRUARY 17, 2023

Curtis Northcutt, CEO and co-founder of Cleanlab, presented the tools his company developed for cleansing data sets prior to model training at the 2022 Future of Data-Centric AI conference. First I’ll chat a bit about millions of label errors and the 10 most common machine learning benchmark data sets. What would we get?

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Snorkel AI

FEBRUARY 17, 2023

Curtis Northcutt, CEO and co-founder of Cleanlab, presented the tools his company developed for cleansing data sets prior to model training at the 2022 Future of Data-Centric AI conference. First I’ll chat a bit about millions of label errors and the 10 most common machine learning benchmark data sets. What would we get?

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The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. Supporting the operations of data scientists and ML engineers requires you to reduce—or eliminate—the engineering overhead of building, deploying, and maintaining high-performance models.

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AWS Machine Learning Blog

OCTOBER 4, 2023

The ability to automate this process using machine learning (ML) techniques allows healthcare professionals to more quickly diagnose certain cancers, coronary diseases, and ophthalmologic conditions. This user-friendly approach eliminates the steep learning curve associated with ML, which frees up clinicians to focus on their patients.

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