Realistic training data for machine learning
Data are the foundation for training algorithms. The more realistic the data, the better the results. This is because artificial intelligence is based on precise and reliable information for training its algorithms. This is obvious but it is often overlooked. The training data are realistic when they reflect the data that the AI system gathers in real operation. Unrealistic data sets prevent machine learning and lead to expensive false interpretations.
Read moreA Snapshot of AI, Machine Learning, and Deep Learning
Real-world examples, prominent use cases, and AI’s (artificial intelligence) potential
The industrialized world is full of machines that outdo humans in strength and speed. Cranes lift steel beams to towering heights, car engines send passengers flying down the road at impossible speeds, and tree shredders pulverize entire pine trunks in a snap. Such inventions replicate and vastly outperform humans at tasks requiring physical exertion. They possess “artificial brawn”.
What then is artificial intelligence?
At a first pass, we can think of AI as a machine (i.e., a computer) replicating human cognitive tasks. A calculator, for example, embodies basic AI to do a job for which humans must use their brains: math.
Read moreImage annotation and artificial intelligence
Everyone has heard something about artificial intelligence. However, the term image annotation is less common. Image annotation describes the classification of information that is of relevance to an image. Recognizing the content of images is an important factor for many automatized processes. In order for machines to capture the meaning and individual components of images, artificial intelligence is required, in which a human-like analysis of images is simulated. To achieve this, countless training data in terms of human input are required.
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