Sustainable Dev’t In Ghana
The latest survey by the Ghana Statistical Service revealed that 42,396 agribusiness firms closed during the lockdown, with 16,091 of those firms still remaining closed. The impact of COVID-19 was more pronounced in the service and industry sectors, particularly for...
The Lack of Diversity in Datasets And Why We Should Care
There has been a noticeable lack of diverse datasets to train machine learning models, resulting in ethical harms against specific communities. AI ethics researchers are pushing for solutions that involve more transparency in model development and dataset training. Regulations on...
Abductive Learning and Artificial Intelligence: Why Can’t Machines learn like Humans?
In an oriental fable, the sons of King Serendippo travel a distant road. They come across a man who has lost a camel. Is it, they ask him, blind on one side, carrying two skins on its back, one of...
The Art of the Dataset
Artists working with AI face multiple decisions during their creative process. Typically, they need to come up with a compelling concept, find a relevant dataset, choose a suitable algorithm and curate the generated images for display. Each stage offers plenty...
Crowdsourcing Vs. Managed Service Vs. In-House Labeling
Labeled data is required for all supervised machine learning projects. Labels are added to raw data, such as images, text, audio, and video, in order to train algorithms to map inputs to outputs. If training is successful, the model will...
How to Find Training Data for Machine Learning
Supervised machine learning projects require training data. By learning from training data, a supervised algorithm aims to be able to accurately predict outcomes when exposed to real data. Training data is required for all types of supervised machine learning projects:...
Manual Vs. Automated Data Labeling
As demands increase for high-quality, large-scale training datasets, data labeling has become an increasingly important function within AI. Manually labeling training data is labor-intensive and can be difficult and expensive (see our guide to data labeling here), but is automatic...
Is Synthetic Training Data the Future of Machine Learning?
Even as the availability of processing power increases exponentially, and machine learning algorithms are commoditized, there is a problem that persistently slows the development of complex AI: obtaining high-quality, accurate training data. All supervised machine learning algorithms require training data,...
Objectivity and Ground Truth in AI
‘Ground truth’ is somewhat of a confusing machine learning buzzword. Discussions of the ground truth naturally touch on areas such as bias, representation, and objectivity, and it’s a concept worthy of discussion. One of the main pitfalls of AI is...