Heartbeat

MARCH 5, 2024

Image by istockphoto Computer vision has become a ground-breaking area in artificial intelligence and machine learning with revolutionary applications. It seeks to mimic perception and comprehension of the visual world by the human Toem. However, annotating vast datasets with accurate labels can be time-consuming and expensive.

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AWS Machine Learning Blog

APRIL 27, 2023

The model is scored correctly if it predicts “no error” or “correct” in the generation if the annotators labeled the example as having no error, or if it predicts any of the error types in the generation (along with “incorrect” or “wrong”) when the annotators labeled it as having an error. He is an ACM Fellow and IEEE Fellow.

Machine Learning 65

Machine Learning Large Language Models Categorization Natural Language Processing 65

Heartbeat

MAY 22, 2023

Computer’s perception of facial expressions Steps of facial emotion recognition [ Source ] Facial expressions and human emotions are tightly coupled. So, for machines to be able to understand the meaning of each emotion, it's important for them to excel in facial detection, and particularly facial landmark detection.

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AWS Machine Learning Blog

FEBRUARY 9, 2023

It involves data labeling, model training, and deployment. Rather than downloading the data to a local machine for inferences, SageMaker does all the heavy lifting for you. Previously he was a senior scientist at Alexa AI, the head of machine learning at Scale AI and the chief scientist at Pony.ai.

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Heartbeat

MARCH 14, 2023

This is crucial to robotics because it allows machines to learn about their environments and act accordingly. Deep learning models for computer vision problems have been extensively trained using ImageNet , a sizable dataset of labeled images. Additional resources: Adrian, R. Deep learning for computer vision with Python.

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Computer Vision Robotics Deep Learning Algorithm 97

Heartbeat

JANUARY 30, 2023

In marketing, businesses can utilize sentiment analysis to comprehend consumer perceptions of their brand, those of their rivals, and market trends. There are several methods for performing sentiment analysis, including rule-based approaches, lexicon-based approaches, and machine-learning approaches. Let’s get started!

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Heartbeat

SEPTEMBER 20, 2023

This technology has broad applications, including aiding individuals with visual impairments, improving image search algorithms, and integrating optical recognition with advanced language generation to enhance human-machine interactions. Essentially, machines are taught to create captions congruent with the photos.

Natural Language Processing 52

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AWS Machine Learning Blog

MAY 2, 2024

If asked to describe the colors on the ends, people would provide varied, subjective responses based on their perceptions. There is a general perception that reward models are often associated only with Reinforcement Learning from Human Feedback (RLHF). It’s important to have diversity in human labelers.

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AWS Machine Learning Blog

JULY 3, 2023

In computer vision (CV), adding tags to identify objects of interest or bounding boxes to locate the objects is called labeling. Hundreds of thousands of work hours are spent generating high-quality labels from images and videos for various CV use cases. For a full list of features, refer to Amazon SageMaker Data Labeling Features.

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Deep Learning Auto-complete Machine Learning ML 81

Pickl AI

JULY 17, 2023

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing models and algorithms that train the machine to think and work like a human. There are two types of Machine Learning techniques, including supervised and unsupervised learning. What is Unsupervised Machine Learning?

Machine Learning 52

Machine Learning Algorithm Categorization Data Analysis 52

AWS Machine Learning Blog

NOVEMBER 10, 2023

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to uncover information in unstructured data and text within documents. Moreover, as LLMs become more integrated into our daily digital experiences, their influence on our perceptions, beliefs, and decisions grows.

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Google Research AI blog

JUNE 18, 2023

If you are attending CVPR this year, please stop by our booth to chat with our researchers who are actively exploring the latest techniques for application to various areas of machine perception.

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Computer Vision Robotics Large Language Models Categorization 116

Topbots

JANUARY 15, 2024

This was based on my perception that both approaches achieved similar results but varied in terms of complexity, cost, and quality. In this blog post, we’ll dive deep into the nuances that differentiate RAG and finetuning across various dimensions that are, in my opinion, crucial in determining the optimal technique for a specific task.

LLM 59

LLM Natural Language Processing Chatbots NLP 59

AWS Machine Learning Blog

MAY 30, 2023

Amazon SageMaker Ground Truth Plus helps you prepare high-quality training datasets by removing the undifferentiated heavy lifting associated with building data labeling applications and managing the labeling workforce. From there, an expert workforce that is trained on a variety of machine learning (ML) tasks labels your data.

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Generative AI Machine Learning Chatbots LLM 66

Google Research AI blog

MAY 15, 2023

How do we effectively annotate skin tone for use in inclusive machine learning (ML)? In addition, Dr. Monk annotated each subject with a skin tone label and then selected a “golden” image for each subject that best represents their skin tone. What does global consensus of skin tone look like?

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Google Research AI blog

FEBRUARY 28, 2023

Few-shot Regional Machine Translation FRMT is a few-shot evaluation dataset containing en-pt and en-zh bitexts translated from Wikipedia, in two regional varieties for each non-English language (pt-BR and pt-PT; zh-CN and zh-TW). VideoCC A dataset containing (video-URL, caption) pairs for training video-text machine learning models.

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Robotics Deep Learning Auto-classification ML 72

Google Research AI blog

DECEMBER 1, 2022

Over the last few years, there have been significant advances in the application of machine learning (ML) for instruction following , both in simulation and in real world systems. Recent Palm-SayCan work has produced robots that leverage language models to plan long-horizon behaviors and reason about abstract goals.

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Robotics ML Machine Learning 52

Chatbots Life

MAY 12, 2023

Unfortunately, the model’s release wasn’t accompanied by a research paper, and its only official description can be found on the OpenAI blog. Reinforcement learning is a machine learning training method based on rewarding desired behaviours and punishing undesired ones. But what is a language model? Because language models are jerks.

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ChatGPT Software Engineer NLP Prompt Engineer 105

Google Research AI blog

JULY 7, 2023

Posted by Sanjay Subramanian, PhD student, UC Berkeley, and Arsha Nagrani, Research Scientist, Google Research, Perception Team Visual question answering (VQA) is a machine learning task that requires a model to answer a question about an image or a set of images.

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Large Language Models Python LLM Artificial Intelligence 84

Sebastian Ruder

JANUARY 24, 2022

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP). However, for many domains large amounts of labelled data are already available, which can be used to learn better representations. Credit for the title image: Liu et al. Prompts are not a silver bullet, however. What’s next?  

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ML Review

JUNE 4, 2018

Image captioning is interesting to us because it concerns what we understand about perception with respect to machines. Establishing this grounding for machines is known as the language grounding problem. Which, once unearthed, could potentially lead to contextually-sophisticated machines.

Neural Network 52

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ML Review

MAY 3, 2018

It could enable processes in machines analogous to how people associate many modalities and experiences with the aforementioned concept of ‘cat’. The addition of language may eventually provide a means for machines to group, reason and articulate complex concepts in the future.

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