Algorithm Annotation Learning 
Smart Data Collective
MAY 31, 2022
Machine learning and artificial intelligence (AI) have certainly come a long way in recent times. Towards Data Science published an article on some of the biggest developments in machine learning over the past century. A number of new applications are making machine learning technology more robust than ever.
Smart Data Collective
JULY 1, 2023
One development that AI has led to is the growth of image annotation. Image annotation is the act of labeling images for AI and machine learning models. It involves human annotators using a tool to label images or tag relevant information. The resulting structured data is then used to train a machine learning algorithm.
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Smart Data Collective
JUNE 22, 2023
Taking the world by storm, artificial intelligence and machine learning software are changing the landscape in many fields. Earlier today, one analysis found that the market size for deep learning was worth $51 billion in 2022 and it will grow to be worth $1.7 Amazon has a very good overview if you want to learn more.
CIO
AUGUST 1, 2022
In a distributed learning setup, data from different hospitals must be brought together to create a centralised data repository for model training, raising lot of concerns on data privacy. Federated learning is a method of training AI algorithms with data stored at multiple decentralised sources without moving that data.
CIO
AUGUST 24, 2023
Multinational data infrastructure company Equinix has been capitalizing on machine learning (ML) since 2018, thanks to an initiative that uses ML probabilistic modeling to predict prospective customers’ likelihood of buying Equinix offerings — a program that has contributed millions of dollars in revenue since its inception.
Smart Data Collective
AUGUST 11, 2022
Human labeling and data labeling are however important aspects of the AI function as they help to identify and convert raw data into a more meaningful form for AI and machine learning to learn. Faster and Better Learning. However, Ai uses algorithms that can screen and handle large data sets.
Smart Data Collective
MAY 29, 2023
This bias can emerge due to multiple factors, such as the training data, algorithmic design, and human influence. Recognizing and comprehending the different forms of algorithm bias is crucial to develop effective strategies for bias mitigation. AI translation models must collect and annotate data fairly.
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