The horizon of what repetitive tasks a computer can replace continues to expand due to artificial intelligence (AI) and the sub-field of deep learning (DL).
Artificial intelligence gives a device some form of human-like intelligence.
Deep learning is an AI technology that has made inroads into mimicking aspects of the human brain — giving a device the ability to process information for contextual analysis and action.
Researchers continue to develop self-teaching algorithms that enable deep learning AI applications like chatbots.
To understand deep learning better, we need to understand it as part of the AI evolution:
The Path To Deep Learning And Reaching Full AI
Taking Machine Learning Farther
Within artificial intelligence, machine learning (ML) is a sub-field — and deep learning is an advanced sub-field of machine learning. In machine learning, a device is able to process and evaluate information beyond its programming based on context. An ML weakness stemming from polluted data sets comes from the fact that ML algorithms rely upon humans to supply the data: Humans categorize the information prior to training the algorithms or provide the algorithms with training feedback. Human subjectivity and biases subsequently creep into the algorithms.
Continue reading: https://www.datamation.com/artificial-intelligence/ai-and-deep-learning/
Artificial intelligence gives a device some form of human-like intelligence.
Deep learning is an AI technology that has made inroads into mimicking aspects of the human brain — giving a device the ability to process information for contextual analysis and action.
Researchers continue to develop self-teaching algorithms that enable deep learning AI applications like chatbots.
To understand deep learning better, we need to understand it as part of the AI evolution:
The Path To Deep Learning And Reaching Full AI
Taking Machine Learning Farther
Within artificial intelligence, machine learning (ML) is a sub-field — and deep learning is an advanced sub-field of machine learning. In machine learning, a device is able to process and evaluate information beyond its programming based on context. An ML weakness stemming from polluted data sets comes from the fact that ML algorithms rely upon humans to supply the data: Humans categorize the information prior to training the algorithms or provide the algorithms with training feedback. Human subjectivity and biases subsequently creep into the algorithms.
Continue reading: https://www.datamation.com/artificial-intelligence/ai-and-deep-learning/
