Tasq.ai promises faster data listing for AI development

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

Data annotation or tagging is one of the most important aspects of building a successful and scalable AI / ML project. It provides the first setup for training a machine learning model with what it needs to understand and how to analyze different inputs to come up with accurate outputs. Most companies usually rely on small internal teams or outsourcing business processes to get their datasets annotated for training. There are also a growing number of other startup companies offering to comment on data, including Snorkel AI, SuperAnnotate and Labelbox.

Tasq.ai says it offers 30x faster data tagging for AI than current methods by combining ML models and proprietary technology to “intelligently deconstruct complex image data.” Once the data is broken down into simple “micro-tasks”, it can be used to exploit what the company says is an untapped, impartial global human workforce of millions to tag and validate data. The company says it can offer unlimited scale without compromising the quality of the dataset or introducing interference.

“We are bringing the utility model that Amazon pioneered for cloud storage to data reporting for AI. It will abolish the way AI is structured and remove the bottlenecks for data that slow down development, ”said Erez Moscovich, co-founder and CEO of Tasq.ai, in a statement.

Lightning fast data reporting for AI projects

Tasqers (annotators), who are responsible for validating results, are only shown relevant parts of images and asked if the image they are looking at contains the object or not, the company says on its website. Tasker’s several judgments are validated, weighted, assessed and aggregated into a structured form with insightful insight.

The platform ropes in annotators through partnerships with leading ad networks, which help identify talent and give them access to premium content as they complete identification tasks. It then uses sophisticated algorithms to train, qualify, test and monitor these digital employees.

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

Why it’s hot right now

Data commenting is a hot investment area because it is still a challenge for so many companies. Data labeling often costs high operating costs as well as inflexibility, bias and inaccuracy from human annotators. People are also slow. These challenges can affect the performance and behavior of AI or another model. It’s like if you show a child lots of different pictures of dogs and tell them it’s a cat, they would continue to identify dogs as cats in the future.

The investment in the two-year company was led by a coupling of angel investors, including Wix’s former AI chief, 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 reporting projects for companies like Here, Intel, FruitSpec, SuperSmart and VHive. Its computer vision solution can be used in a variety of areas, 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 scale of the massive data annotation problem with data annotation, which Tasq.ai is the first to address,” said Professor Shai Dekel in the statement.

“They alone are at the forefront of data listing, and that’s a huge achievement and advantage, not to mention a huge leap forward for the development of AI. Tasq.ai’s success means expanding access to the ability to quickly build great AI and more efficient applications that will benefit both businesses and users, ”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 largest economic gains to $ 10.7 trillion.

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