Machine Learning Research at Apple

MAY 10, 2023

Self-training has been shown to be helpful in addressing data scarcity for many domains, including vision, speech, and language. Specifically, self-training, or pseudo-labeling, labels unsupervised data and adds that to the training pool.

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Flipboard

DECEMBER 16, 2023

How can data labeling be done efficiently? Successful machine learning models are built on the shoulders of large volumes of high-quality training …

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KDnuggets

DECEMBER 13, 2021

So much of data science and machine learning is founded on having clean and well-understood data sources that it is unsurprising that the data labeling market is growing faster than ever.

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Smart Data Collective

JUNE 22, 2023

One such field is data labeling, where AI tools have emerged as indispensable assets. This process is important if you want to improve data quality especially for artificial intelligence purposes. This article will discuss the influence of artificial intelligence and machine learning in data labeling.

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KDnuggets

DECEMBER 4, 2019

Let’s say your project is humongous and needs data labeling to be done continuously - while you’re on-the-go, sleeping, or eating. I’m sure you’d appreciate User-generated Data Labeling. I’ve got 6 interesting examples to help you understand this, let’s dive right in!

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Dataversity

SEPTEMBER 25, 2023

In an era where large language models (LLMs) are redefining AI digital interactions, the criticality of accurate, high-quality, and pertinent data labeling emerges as paramount. That means data labelers and the vendors overseeing them must seamlessly blend data quality with human expertise and ethical work practices.

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Smart Data Collective

SEPTEMBER 17, 2023

Big data technology has disrupted the marketing profession in countless ways. We have talked extensively about the benefits of data analytics in SEO. It can be especially helpful with white label SEO. It can be especially helpful with white label SEO. billion in 2021 to $6.4 billon by 2026.

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Smart Data Collective

OCTOBER 9, 2023

Big data technology has had a profound impact on many sectors. One of the fields that is heavily affected by advances in big data is the manufacturing industry. We have talked at length about some of the ways that manufacturers are using big data and AI to improve the trajectory of their industry.

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Snorkel AI

SEPTEMBER 29, 2023

Data labeling remains a core requirement for any organization looking to use machine learning to solve tangible business problems, especially with the increased development and adoption of LLMs. This is where data labeling fits in. What is data labeling? This approach applies across all data modalities.

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Pickl AI

AUGUST 7, 2023

However, the most complex and expensive type of Machine Learning trend in use is Labelled Data. They require human expertise to tell a machine about the different data types to help Artificial Intelligence learn it. Both labeled and unlabeled data are used in Machine Learning for different purposes.

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Towards AI

APRIL 27, 2023

80% of the time goes in data preparation ……blah blah…. In short, the whole data preparation workflow is a pain, with different parts managed or owned by different teams or people distributed across different geographies depending upon the company size and data compliances required. What is the problem statement? blah blah…….

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FlowingData

JULY 18, 2023

For Bloomberg, Davey Alba reports on how some of that magic is just a bunch of people labeling data for low wages : Other technology companies training AI products also hire human contractors to improve them. In January, Time reported that laborers in Kenya, paid $2 an hour, had worked to make ChatGPT less toxic. Amazon.com Inc.

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KDnuggets

OCTOBER 31, 2019

AI-based models are highly dependent on accurate, clean, well-labeled, and prepared data in order to produce the desired output and cognition. These models are fed with bulky datasets covering an array of probabilities and computations to make its functioning as smart and gifted as human intelligence.

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insideBIGDATA

MAY 21, 2023

analysis, the global label classifier market is expected to be worth US$ 48.89 During the forecast period, the label classifier industry is predicted to expand at a CAGR of 27.3%. The label classifier market is anticipated to increase significantly as AI and ML technologies become widely used. As per a new Fact.MR

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KDnuggets

APRIL 28, 2023

Reduce LLM prediction error by 37% via data-centric AI.

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Flipboard

JULY 3, 2023

Additionally, generative models are frequently used in a variety of sectors to generate data for different applications. The second category of options turns to outside sources of data, such as hiring human reviewers to verify the answer or using huge amounts of labeled data to create assessment models.

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Analytics Vidhya

JULY 12, 2023

One often encounters datasets with categorical variables in data analysis and machine learning. This is where label encoding comes into play. By transforming category data into numerical labels, label encoding enables us to use them in various algorithms. […] The post How to Perform Label Encoding in Python?

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Hacker News

APRIL 11, 2024

ISPs comply with FCC rule after protesting requirement to list all fees.

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Dataversity

JANUARY 10, 2022

The post 2022 AI Trends: Humans and AI Unite, RPA Fades, and Data Labeling Returns appeared first on DATAVERSITY. It was a dynamic year for those in the AI industry […].

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Analytics Vidhya

SEPTEMBER 7, 2023

Introduction In the dynamic world of machine learning, one constant challenge is harnessing the full potential of limited labeled data. Enter the realm of semi-supervised learning—an ingenious approach that harmonizes a small batch of labeled data with a trove of unlabeled data.

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KDnuggets

FEBRUARY 12, 2024

You don't always have high-quality labeled datasets for supervised machine learning. Learn about why you should augment your real data with synthetic data as well as the ways to generate it.

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Analytics Vidhya

JANUARY 27, 2024

Introduction Pandas is a powerful data manipulation library in Python that provides various data structures, including the DataFrame. A DataFrame is a two-dimensional labeled data structure with columns of potentially different types. It is similar to a table in a relational database or a spreadsheet in Excel.

Data Analysis 295

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Smart Data Collective

APRIL 2, 2021

Big data has been a gamechanger in the ecommerce sector in recent years. One of the biggest benefits of using big data to create a successful ecommerce channel is that it helps show which products are performing the best. There are a lot of ways to use big data for an ecommerce business model. What are white label products?

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Analytics Vidhya

JANUARY 30, 2024

Introduction Pandas is a powerful data manipulation library in Python that provides various functionalities for working with structured data. One of its critical features is its ability to handle and manipulate DataFrames, which are two-dimensional labelled data structures.

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KDnuggets

JULY 25, 2019

Analyst firm Cognilytica estimates that as much as 80% of machine learning project time is spent on aggregating, cleaning, labeling, and augmenting machine learning model data.

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Data Science Dojo

APRIL 29, 2024

Generative AI refers to a branch of artificial intelligence that focuses on creating new content—be it text, images, audio, or synthetic data. These AI systems learn from large datasets to recognize patterns and structures, which they then use to generate new, original outputs similar to the data they trained on.

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KDnuggets

DECEMBER 21, 2021

Customizable, real-time data labeling pipelines that can continuously receive and process unlabeled data are necessary to train and perfect the AI that impacts our lives and daily conveniences.

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Machine Learning Research at Apple

MAY 5, 2024

Vision Foundation Models (VFMs) pretrained on massive datasets exhibit impressive performance on various downstream tasks, especially with limited labeled target data. However, due to their high inference compute cost, these models cannot be deployed for many real-world applications.

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Eugene Yan

MARCH 11, 2023

Writing good instructions to achieve high precision and throughput.

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KDnuggets

SEPTEMBER 9, 2019

I am really interested in creating a tight, clean pipeline for disaster relief applications, where we can use something like crowd sourced building polygons from OSM to train a supervised object detector to discover buildings in an unmapped location.

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Machine Learning Research at Apple

APRIL 28, 2024

Deep learning models can perform the task but at the expense of large labeled datasets, which are unfeasible to procure at scale.

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Analytics Vidhya

NOVEMBER 17, 2022

This article was published as a part of the Data Science Blogathon. Source: Canva Introduction Competitive Deep Learning models rely on a wealth of training data, computing resources, and time. However, there are many tasks for which we don’t have enough labeled data at our disposal.

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Analytics Vidhya

MARCH 5, 2020

When should you use One-Hot Encoding over Label Encoding? These are typical data science interview questions every aspiring data scientist. The post One-Hot Encoding vs. Label Encoding using Scikit-Learn appeared first on Analytics Vidhya. What is One-Hot Encoding?

Data Scientist 314

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KDnuggets

FEBRUARY 6, 2023

Collecting and labeling data in the real world can be time-consuming and expensive. This data can also come with quality, diversity, and quantity issues. Fortunately, problems like these can be helped with synthetic data.

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Analytics Vidhya

JULY 15, 2022

This article was published as a part of the Data Science Blogathon. The post Segmentation of Wheat Crops using MLIB’s K-Means appeared first on Analytics Vidhya.

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KDnuggets

JANUARY 13, 2022

Any modern company of any significant size around the world has a data science department, and a data engineer at one company might have the same responsibilities as a marketing scientist at another company. Data science jobs are not well-labeled, so make sure to cast a wide net.

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