Tasq.ai promises faster data annotation for AI development
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Israel-based Tasq.ai, a startup 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 financing.
Data annotation or labeling is one of the most important aspects of building a successful and scalable AI/ML project. It provides the initial setup for training a machine learning model with what it needs to understand and how to analyze various inputs to come up with accurate outputs. Most companies usually rely on small internal teams or business process outsourcing to get their dataset annotated for training. There is also a growing number of other startup companies offering to annotate data, including Snorkel AI, SuperAnnotate and Labelbox.
Tasq.ai says it offers 30x 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 on the quality of the dataset or bringing in 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,” Erez Moscovich, co-founder and CEO at Tasq.ai, 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 asked whether or not the image they are looking at contains the 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, which help identify the talent and provide them access to premium content when they complete identification tasks. It then uses sophisticated algorithms to train, qualify, test, and monitor these digital workers.
The Tasq.ai service is 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 at a high cost of operation, as well as inflexibility, bias, and inaccuracy by human annotators. Humans are also slow. These challenges can affect the performance and behavior of the AI or other model in question. It’s like if you show a child lots of different images of dogs and tell them it’s a cat, they would go on to identify dogs as cats in the future.
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 new sales offices in New York and Chicago. It also plans to accelerate R&D efforts in Israel to improve its solutions, a statement said.
So far, Tasq has already handled data annotation projects for companies like Here, Intel, FruitSpec, SuperSmart, and VHive. Its computer vision solution can be applied in a range of areas, starting from autonomous vehicles and drones to e-commerce, 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,” Professor Shai 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 would be China and North America which will drive the greatest economic gains at $10.7 trillion.
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