The benefits of using artificial intelligence for data labeling
Labelbox is a leading artificial intelligence startup recently closing $40 million in Series C funding. This is a huge milestone for the AI industry and showcases AI’s growing importance in data labelling.
Let’s look at why data labelling is so important and how AI can help make it easier.
Definition of Artificial Intelligence
Artificial intelligence (AI) is the study and application of computer algorithms that allow machines to learn from data and experience to solve problems, make decisions, and act autonomously. AI is a rapidly developing field with the potential to revolutionise industries such as healthcare, finance, transportation, manufacturing, retail and more by dramatically improving business operations.
Recently, there has been increasing interest in using artificial intelligence for data labelling purposes. Data labelling is assigning labels or labels such as tags to data for various uses; for example, categorising images into classes like medical scans or identifying text sentiment. By leveraging machine learning algorithms such as deep learning neural networks to automatically process datasets consisting of both structured and unstructured data points – or in other words “labelling” – companies can quickly train their AI models on vast amounts of labelled data to enable them to make predictions with greater accuracy faster than ever before. In addition, artificial intelligence can be used for predictive modelling scenarios in which an algorithm can predict outcomes based on previously labelled or trained data sets.
Recently, Artificial Intelligence startup Labelbox closed $40 million series C funding led by Sequoia Capital India to further develop their AI-powered software solutions for enterprise-level applications such as object detection from images or voice transcription from audio recordings. The company’s technology enables enterprises worldwide to quickly label datasets with minimal labor costs by leveraging machine learning technology for their digital transformation efforts. With more businesses investing in artificial intelligence solutions for labelling projects due its multiple benefits like improved accuracy and reduced time – it’s not too difficult to see why Labelbox secured such a large investment round!
Overview of Labelbox
Labelbox is an artificial intelligence (AI) startup that specialises in data labelling for machine learning. The company just closed a Series C funding round of $40 million, raising its total amount to more than $80 million. Labelbox’s success reflects the larger growth in AI technology and the increasing demand for reliable and accurately labelled datasets used by AI applications.
Labelbox leverages advanced machine learning and annotation technology to quickly process large amounts of data while ensuring accuracy and consistency across thousands of datasets. Their platform helps businesses power algorithms, automate data entry processes, and create interactive reports with real-time metrics on their digital products.
Labelbox’s customer base spans various industries, including autonomous vehicles, healthcare, retail, media & entertainment and financial services. In addition, the company has made several strategic partnerships with leading organisations such as Microsoft Azure AI Labs, Pivotal Software Inc., NVIDIA NVidia GPUs and MediaTek Labs.
In addition to their sophisticated technology platform, Labelbox offers managed services that provide advice from their expert team on labelling best practices and scalable solutions depending on specific business needs. Furthermore, their cutting-edge features such as task automation enable customers to manage a large volume of tasks more efficiently while maintaining the highest accuracy level.
Benefits of AI for Data Labelling
Artificial Intelligence (AI) is becoming an increasingly important tool for businesses to develop better, faster and more efficient tools to manage and process data. As a result, AI-powered startups like Labelbox are seeing a surge in investments, with Labelbox recently raising $40 million in Series C funding.
This article will discuss the key benefits of using AI for data labelling.
Using Artificial Intelligence (AI) for data labelling offers numerous benefits over manual processes. While most manual data labelling processes require field annotations prone to human error, AI data labelling is automatic, consistent, accurate and can even provide enhanced semantic understanding of content. Furthermore, as AI algorithms are already trained with many online datasets such as Open Images and ImageNet, they can identify objects in images faster and more accurately than humans.
AI based data labelling techniques like transfer learning utilise background knowledge to create a general model. This allows the model to learn and extract important features by identifying common characteristics in an image’s different categories. For example, transfer learning could better identify features like circles or arrows for a traffic sign than an inexperienced labeler who has never seen this type of image before.
AI can also detect multiple objects in an image at once and analyse them from various angles without requiring further instruction from a human. This feature drastically increases the accuracy of data labelling compared to manual methods which usually require people to manually inspect each object individually to ensure accuracy. In addition, by automating tedious parts of manual data annotation tasks such as evaluating symbols or recognizing patterns, AI systems allow teams to increase their efficiency while reducing overall costs associated with labour-intensive tasks.
With these capabilities, commercial artificial Intelligence startups such as Labelbox are gradually replacing traditional annotation techniques by offering increased accuracy at scale on complex datasets due to automated workflows enabled by deep learning algorithms included within their platform solutions.
Reduced Human Error
Data labelling via human manual labour has traditionally been the go-to method for precisely labelling items in a data set. Unfortunately, this method is prone to human errors such as inconsistencies in parsing, oversights, mistranslations, etc.
Using artificial intelligence (AI) technology to perform data labelling and annotation can significantly reduce human errors that often arise when performing such operations manually. For example, AI models trained to recognize certain features within images can rapidly parse different objects and accurately identify them – even if they’re partially obscured or out of focus. As a result, providing precise labels on items within a dataset saves time and money, eliminating the need for costly revisions or having to re-label entire datasets due to incorrect labels.
Moreover, when using AI for data labelling projects, organisations can achieve incredible accuracy without wasting their time or resources managing an ever-growing team of manual labourers — ultimately reducing their overall financial burden. This is why startups like Labelbox have seen considerable growth after incorporating AI into their data annotation workflow — recently closing $40 million in series C funding at the end of 2020.
One of the obvious advantages of using artificial intelligence (AI) for data labelling is the increase in efficiency. Automation can automate many of the mundane and manual processes related to data labelling. For example, AI enables faster recognition and extraction of images or objects, while ensuring accurate, consistent and granular tagging with little human effort. This greatly speeds up the data labelling process and many additional steps in a machine learning process such as feature engineering and model training.
Another way that AI-driven data labelling leads to increased efficiency is by enabling machines to continue tagging without humans having to examine every image for accuracy or agreement between two different tags which on rare occasions can disagree. Furthermore, AI-driven automation allows more images or objects to be tagged at once than humans can analyse quickly. Decreasing the amount of time spent manually tagging each image or object significantly speeds up development times for an artificial intelligence project as there’s less manual labour required throughout the process. AI-based solutions such as Labelbox critically increase ML development speed due to decreased painstaking manual labour typically involved with data labelling by allowing organisations far easier access to high quality labelled datasets they wouldn’t have been able to get on their own otherwise.
Artificial Intelligence Startup Labelbox Closes $40 Million in Series C Funding
Artificial Intelligence (AI) start-up Labelbox recently closed $40 million in Series C funding, demonstrating the growing importance of AI for data labelling.
While data labelling is a challenge for many companies, AI-powered solutions like Labelbox offer businesses a way to automate the process and ensure accuracy.
In this article, we’ll explore the advantages of using AI for data labelling.
Overview of Series C Funding
Labelbox, an artificial intelligence data labelling platform, recently announced closing $40 million in Series C funding. Coatue led the latest round of investment. It included participation from new investors Costanoa Ventures and New Enterprise Associates (NEA) and existing Gradient Ventures, Amplify Partners and Y Combinator. The additional capital infusion brings Labelbox’s total funding to $70.2 million since its inception in 2018.
The additional funds will be used to expand product development, fuel growth initiatives such as strategic acquisitions, bolster the firm’s marketing & sales efforts and pursue new go-to-market activities.
The influx of capital will allow Labelbox to enhance its machine learning data labelling capabilities by providing customers with automated corrections and annotations, reducing human effort across the data labelling process for machine learning initiatives using large volumes of high-quality training data quickly. This is an invaluable resource for businesses leveraging AI technologies for cutting-edge analytics applications. Additionally, the extra capital can provide more opportunities for research collaboration with universities including the Georgia Institute of Technology (Georgia Tech) and Stanford University. Furthermore, this will connect customers from multiple industries such as life sciences, healthcare and financial services that require accurate real-world datasets for their deep learning research models and applications.
Benefits of Series C Funding
The latest round of financing was led by Bond Capital and brought the total investment up to $73 million. This injects significant capital into the AI startup Labelbox, which specialises in data labelling and computer vision technology.
This infusion of funds will continue empowering businesses worldwide to create large-scale, high-quality data automation projects through artificial intelligence. AI allows significantly faster results with higher accuracy than manual or semi-automatic labelling processes.
Series C funding brings several advantages that could benefit Labelbox’s current and future customers:
- It gives businesses more stability by allowing more capital for research and development, allowing them to create new product developments.
- It creates economies of scale that allow companies to price their services competitively.
- It strengthens the company’s brand recognition and provides validation for existing product offerings as well as trust in new ones due to the large financial investment from external sources
- It allows companies more flexibility with their cash flow, allowing them to take on more customers
- It positions Labelbox for potential acquisition by larger firms further down the road.
tags = Catherine Wood, CEO and founder of ARK Invest., Labelbox has raised $79 million in venture funding, Andres Pretio-Moreno, Director, Corporate Technology Advanced Projects at FLIR, 110m softbank vision fund labelboxcaiforbes, 110m softbank fund ceo labelboxcaiforbes