Tag: data labeling
Feb 18, 2021 |
Progress in AI technology promises to usher in a new era of automation and innovation. But will advances in machine intelligence and the march toward automation displace human intelligence?
Half of businesses have already adopted AI technology to streamline at least one function, according to a recent McKinsey survey, and Gartner projects that 90% of enterprises will have brought aboard an automation architect by 2025. Read more…
Oct 16, 2020 |
Data is the fuel for machine learning, but the data needs to be accurately labeled for the machines to learn. To that end, data training startup Dataloop yesterday unveiled that it’s received $11 million in Series A funding to build SaaS data pipelines that combine human supervision of the data annotation process, along with data management capabilities. Read more…
Jun 10, 2020 |
Amazon Web Services has added a 3D visualization capability to its SageMaker data labeling tool used to build training data sets for machine learning models.
AWS said this week its SageMaker data labeling service called Ground Truth introduced in 2018 now includes a workflow for labeling of point clouds, a set of data points generated by tools like 3D scanners or Lidar sensors. Read more…
Feb 11, 2020 |
Data is powerful, but labeling data makes it useful. Labeled data (data that has been appended with informative tags about its contents – say, whether a photo is of a person or an animal) can be used to quickly train machine learning models for identification. Read more…
Dec 18, 2019 |
At the beginning of the year, we set out 10 big data trends to watch in 2019. We correctly called some of what unfolded, including a renewed focus on data management and continued rise of Kubernetes (that wasn’t hard to see). Read more…
Jun 13, 2019 |
We may be living in the fourth industrial age and on cusp of huge advances in automation powered by AI. But according to the latest data, our great future will be less rosy if enterprises don’t start doing something about one thing in particular: Read more…
Jun 25, 2018 |
Building high-quality artificial intelligence (AI) is hard work. It’s a specialized discipline that historically has required highly skilled specialists, aka data scientists.
Any time you require some highly skilled, highly paid practitioner to accomplish something of value, you’ve introduced a bottleneck into that process. Read more…
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