March 21, 2023
The world is in awe of the surprising and versatile interactions with Large Language Models such as ChatGPT — from popping out quirky lyrics to passing bar exams, it’s no wonder that Language AI has captured our imagination. What’s fueling this revolution?
In this article, you will read about:
In January 2023, ChatGPT reached over 100 million users, making it the fastest growing consumer application to date. Tech giants are following suit — Google is
to launch Bard AI, while Microsoft has already
a limited preview of Bing with ChatGPT.
Advancements in Language AI are driven by Natural Language Processing (NLP) and Machine Learning (ML) — both are technologies that enable computers to process, understand and speak human language. The global NLP market alone is
to reach an expected value of USD 91 billion by 2030, growing at a compound annual growth rate (CAGR) of 27%. Similarly, ML market is
to grow from $21.17 billion in 2022 to $209.91 billion by 2029, with a CAGR of 38.8%
Both NLP and ML work with data, which is
the real source of magic
behind Large Language Models.
But not just any data. The success of language AI applications is directly connected to the
quality of the training data
used to develop them.
Training data is a set of examples used to teach a machine learning model to make accurate predictions. The model uses the
in the training data to learn how to map inputs to the correct outputs. It serves as the foundation of the entire project and provides the basis for their models to learn from.
For example, in
a sentiment analysis task
, the training data might consist of a set of reviews along with their corresponding sentiment labels, such as:
The model then uses this data to learn how to predict the sentiment of new reviews.
In another example, for
a language translation task
, the training data would consist of sentence pairs in the source language and their translations in the target language, such as
Spring is coming – La primavera está llegando
. The model uses this data to learn how to translate new sentences.
The more quality samples we offer the machine, the more accurate will be the output. For instance, ChatGPT - 3 was trained on 176 billion parameters, totalling 570 GB of books, articles, websites and other textual data scraped from the Internet.
For algorithms to effectively learn and make precise predictions using these various types of training data, it is essential that the data is pre-labelled with relevant tags or annotations.
Data labeling is a crucial step in the development of many machine learning and artificial intelligence applications, as it ensures the accuracy and quality of the data used to train these systems. It is important to have high-quality, accurate and consistent data labels in order to train effective machine learning models.
Data labeling is the process of assigning one or more tags or labels to a given dataset to enable the classification, organization, and retrieval of data. It involves manually annotating the data with meaningful and relevant metadata, which helps in making the data understandable and accessible to machines for training and analysis. It can involve different types of tasks such as object recognition, image and speech recognition, sentiment analysis, and text classification, among others.
Training data can come from a variety of sources, depending on the particular task or application being considered. Here are some examples:
Data that has been labeled by humans, typically through a process of manual annotation. For example, a dataset of images might be labeled with the objects or scenes present in each image, or a dataset of text might be labeled with the sentiment or topic of each piece of text.
Platforms like Amazon Mechanical Turk or CrowdFlower can be used to collect labeled data from a large number of people at a relatively low cost. This is particularly useful for tasks that require a large amount of labeled data, such as image or speech recognition.
Data can be gathered from websites by using automated tools to extract relevant information. This can be useful for tasks like text classification or sentiment analysis.
In applications such as self-driving cars or health monitoring, data can be collected from sensors such as cameras, LIDAR, or accelerometers.
There are many existing datasets that have been created and made publicly available, such as the ImageNet dataset for image recognition or the MNIST dataset for handwritten digit recognition.
The race for more powerful AI models is fundamentally a race for quality and reliable training data. AI algorithms are becoming more sophisticated and capable, so they require vast amounts of high-quality data to learn from. In fact, access to high-quality training data has become a key differentiator in the development of AI technologies. As the competition to develop the most advanced AI systems intensifies, the ability to collect, label, and process high-quality training data has become an increasingly critical factor in achieving innovation and competitive advantage.
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