In a nondescript office building on the outskirts of Zaporizhzhia, Ukrainian soldiers have been honing what they believed will be a decisive weapon in their effort to repel the Russian invasion. Inside, the weapon glows from a dozen computer screens – a constantly updated portrayal of the evolving battlefield to the south. With one click on a menu, the map is populated with hordes of orange diamonds, showing Russian deployments. They reveal where tanks and artillery have been hidden, and intimate details of the units and the soldiers in them, gleaned from social media. Zooming in shows satellite imagery of the terrain in sharp detail.

Data annotation, or the process of adding labels to images, text, audio and other forms of sample data, is typically a key step in developing AI systems. The vast majority of systems learn to make predictions by associating labels with specific data samples, like the caption "bear" with a photo of a black bear. A system trained on many labeled examples of different kinds of contracts, for example, would eventually learn to distinguish between those contracts and even extrapolate to contracts that it hasn't seen before. The trouble is, annotation is a manual and labor-intensive process that's historically been assigned to gig workers on platforms like Amazon Mechanical Turk. But with the soaring interest in AI -- and in the data used to train that AI -- an entire industry has sprung up around tools for annotation and labeling. Dataloop, one of the many startups vying for a foothold in the nascent market, today announced that it raised $33 million in a Series B round led by Nokia Growth Partners (NGP) Capital and Alpha Wave Global.

Data annotation, or the process of adding labels to images, text, audio and other forms of sample data, is typically a key step in developing AI systems. The vast majority of systems learn to make predictions by associating labels with specific data samples, like the caption "bear" with a photo of a black bear. A system trained on many labeled examples of different kinds of contracts, for example, would eventually learn to distinguish between those contracts and even extrapolate to contracts that it hasn't seen before. The trouble is, annotation is a manual and labor-intensive process that's historically been assigned to gig workers on platforms like Amazon Mechanical Turk. But with the soaring interest in AI -- and in the data used to train that AI -- an entire industry has sprung up around tools for annotation and labeling.

From autonomous drones to driver-assist cars and shopping carts that can check you out of a supermarket without standing in line for a cashier, artificial intelligence is set to change our lives. But it doesn't just happen. In order to function, artificial intelligence needs to recognize millions of traffic hazards, or airborne objects, or items in a supermarket by comparing them with labeled images stored in its memory. Dataloop is the first company to figure out how to combine and automate much of the process behind the "computer vision" that allows these applications to work – saving time and resources. The four-year-old Israeli startup estimates its software is already used by most of the labeling providers.

Machine learning (ML) applications are transforming business strategy, popping up in every vertical and niche to convert huge datasets into valuable predictions that guide executives to make better business decisions, seize opportunities, and spot and mitigate risks. While ML models are rife with potential, it's quality data that allows them to become accurate and effective. Today's enterprises are handling huge floods of data, including unstructured data, all of which needs annotating before ML models can produce dependable predictions. Data processing is often under-scrutinised, but it's crucial for accurate and relevant forecasts. If data is mislabeled or annotated incorrectly, all your predictions will be based on misconceptions, making them basically untrustworthy.

AI data management and annotation startup Dataloop today announced that it raised $16 million in funding, a combination of an $11 million series A round and a previously undisclosed $5 million seed round. A spokesperson says the funds will enable Dataloop to increase its recruitment efforts and grow its presence in the U.S. and Europe. Training AI and machine learning algorithms requires plenty of annotated data. But data rarely comes with annotations. The bulk of the work often falls to human labelers, whose efforts tend to be expensive, imperfect, and slow. Dataloop claims to solve the annotation challenge with a platform for automating data prep and data operations.

Machine learning is a powerful paradigm many organizations are utilizing to derive insights and add features to their applications, but using it requires skills, data, and effort. Explorium, a startup from Israel, has just announced $19 million of funding to lower the barrier on all of the above. The funding announced today comprises a seed round of $3.6 million led by Emerge with the participation of F2 Capital and a $15.5 million Series A led by Zeev Ventures with the involvement of the seed investors. Explorium was founded by Maor Shlomo, Or Tamir, and Omer Har, three Israeli tech entrepreneurs, who previously led large-scale data mining and optimization platforms for big data-based marketing leaders. "We are doing for machine learning data what search engines did for the web," said Explorium co-founder and CEO Maor Shlomo.