Tasq.ai promises faster data annotation for AI development

Israel-based Tasq.ai, which says it has found a much faster way for companies to embark upon data annotation for AI development, today announced it has raised $4 million in seed funding.

Data annotation or labeling is one of the most important aspects of building a successful and scalable AI/ML project. This work provides the initial setup to train a machine learning model on what it needs to understand and analyze to come up with accurate outputs. Many companies rely on small internal teams or business process outsourcing to get their dataset annotated for training. A growing number of other startups are also offering to annotate data, including Snorkel AI, SuperAnnotate, and Labelbox.

But Tasq.ai claims it offers 30 times faster data labeling for AI than current methods by combining ML models and proprietary technology to “intelligently deconstruct complex image data.” Once the data is broken into simple “micro-tasks,” it’s gamified to leverage what the company says is an untapped, unbiased global human workforce of millions to label and validate data. The company says it can offer unlimited scale without compromising the quality of the dataset or introducing biases.

“We’re bringing the usage model that Amazon pioneered for cloud storage to data annotation for AI. It’s going to completely upend the way AI is built and eliminate the data bottlenecks that are slowing progress,” Tasq.ai cofounder and CEO Erez Moscovich said in a statement.

Lightning-fast data annotation for AI projects

Tasqers (annotators) responsible for validating results are only shown relevant portions of images and are asked whether the image they are looking at contains a particular object, the company says on its website. The Tasqers’ multiple judgments are validated, weighted, rated, and aggregated into a structured schema of actionable insights.

The platform ropes in annotators through partnerships with leading ad networks that help identify talent and provide access to premium content when they have completed identification tasks. Tasq.ai then uses sophisticated algorithms to train, qualify, test, and monitor these digital workers.

The service is currently available on a usage-based pricing model.

Why this is hot right now

Data annotation is a hot area of investment because it remains a challenge for so many companies. Data labeling often comes with high operational costs, as well as inflexibility, bias, and inaccuracy on the part of human annotators. Humans are also slow. These challenges can affect the performance and behavior of the AI or other model in question.

The investment in the two-year-old company was led by a clutch of angel investors, including Wix’s former AI head, professor Shai Dekel. The company said it will use the investment to expand its international presence and open sales offices in New York and Chicago. It also plans to accelerate R&D efforts in Israel, according to a statement.

Tasq.ai has already handled data annotation projects for companies like Here, Intel, FruitSpec, SuperSmart, and VHive. Its computer vision solution can be applied to a range of areas, from autonomous vehicles and drones to ecommerce, agriculture, and media.

“Everyone knows that AI capabilities are a must-have, but only those of us who have built AI companies and products understand the extent of the massive data annotation bottleneck issue that Tasq.ai is the first to solve,” Dekel said in the statement.

“They’re alone at the forefront of the data annotation field, and that’s a tremendous achievement and advantage, not to mention a big leap forward for the development of AI. Tasq.ai’s success means expanding access to the ability to quickly build great AI and more effective applications that will be a boon to businesses and users alike,” he added.

According to a PwC study, AI is expected to contribute $15.7 trillion to the global economy by 2030. Leading this growth are China and North America, which will drive the greatest economic gains at $10.7 trillion.